Separable discrete functions: recognition and sufficient conditions

Size: px
Start display at page:

Download "Separable discrete functions: recognition and sufficient conditions"

Transcription

1 Seprle isrete funtions: reognition n suffiient onitions Enre Boros Onřej Čepek Vlimir Gurvih Novemer 21, 217 rxiv: v1 [mth.co] 17 Nov 217 Astrt A isrete funtion of n vriles is mpping g : X 1... X n A, where X 1,...,X n, n A re ritrry finite sets. Funtion g is lle seprle if there exist n funtions g i : X i A for i = 1,...,n, suh tht for every input x 1,...,x n the funtion g(x 1,...,x n ) tkes one of the vlues g 1 (x 1 ),...,g n (x n ). Given isrete funtion g, it is n interesting prolem to sk whether g is seprle or not. Although this seems to e very si prolem onerning isrete funtions, the omplexity of reognition of seprle isrete funtions of n vriles is known only for n = 2. In this pper we will show tht slightly more generl reognition prolem, when g is not fully ut only prtilly efine, is NP-omplete for n 3. We will then use this result to show tht the reognition of fully efine seprle isrete funtions is NP-omplete for n 4. The generl reognition prolem ontins the ove mentione speil se for n = 2. This se is well-stuie in the ontext of gme theory, where (seprle) isrete funtions of n vriles re referre to s (ssignle) n-person gme forms. There is known suffiient onition for ssignility (seprility) of two-person gme forms (isrete funtions of two vriles) lle (wek) totl tightness of gme form. This property n e teste in polynomil time, n n e esily generlize oth to higher imension n to prtilly efine funtions. We will prove in this pper tht wek totl tightness implies seprility for (prtilly efine) isrete funtions of n vriles for ny n, thus generlizing the ove result known for n = 2. Our proof is onstrutive. Using grph-se isrete lgorithm we show how for given wekly totlly tight (prtilly efine) isrete funtion g of n vriles one n onstrut seprting funtions g 1,...,g n in polynomil time with respet to the size of the input funtion. Keywors: seprle isrete funtions, totlly tight n ssignle gme forms MSIS Dep. of RBS n RUTCOR, Rutgers University, 1 Rokfeller Ro, Pistwy, NJ , USA. (enre.oros@rutgers.eu) Deprtment of Theoretil Informtis n Mthemtil Logi, Chrles University, Mlostrnské nám. 25, 118 Prh 1, Czeh Repuli. (onrej.epek@mff.uni.z) MSIS Dep. of RBS n RUTCOR, Rutgers University, 1 Rokfeller Ro, Pistwy, NJ , USA; Ntionl Reserh University Higher Shool of Eonomis, Mosow Russi. (vlimir.gurvih@rutgers.eu) 1

2 1 Introution A isrete funtion of n vriles is mpping g : X 1... X n A, where X 1,...,X n, n A re ritrry finite sets. Disrete funtion g is lle seprle if there exist n seprting funtions of one vrile eh, g i : X i A for i = 1,...,n, suh tht g(x) = g 1 (x 1 ) or... or g(x) = g n (x n ) for every input x = (x 1,...,x n ) X 1 X n. To see some simple exmples for seprle n non-seprle isrete funtions onsier the following isrete funtions of two vriles. For n = 2 we n interpret isrete funtion s n rry, where the rows re inexe y X 1, the olumns y X 2, n the entries of the rry re the g(x 1,x 2 ) A vlues for x 1 X 1 n x 2 X 2. [ ] [ ] [ ] (1) The first two exmples re lerly seprle (we leve to the reer the esy tsk of efining the two seprting funtions, i.e. of ssigning the orret vlues to rows n olumns) while the lst three exmples re not (whih is esy to prove y piking n entry in the rry, ssigning this vlue first to the orresponing row n then to the orresponing olumn, n following in oth ses the sequene of fore ssignments to ontrition). The onept of seprility n e nturlly extene to prtilly efine isrete funtions of n vriles y requiring the ove property only for those inputs for whih the funtion is efine. Note tht the seprting funtions n e lwys ssume to e fully efine - inee if set of n prtilly efine seprting funtions fulfills the ove onition, then so oes ny ompletion of this set to fully efine funtions. Severl onepts of eomposility for prtilly efine Boolen funtions ( speil sulss of isrete funtions) re surveye, for exmple, in [3]. Although isrete funtions re generl onept foun in mny res of isrete mthemtis, our motivtion n interest in stuying them me from gme theory, n so we will in the rest of this pper swith to the stnr gme theoretil terminology. We enote y I = [n] = {1,...,n} the set of plyers, y X i the set of strtegies of plyer i I, n y A the set of possile outomes. A isrete funtion g : X 1... X n A of n vriles is lle n n-person gme form. In other wors, one ll plyers hose prtiulr strtegy, sy x i X i for i I, then g(x 1,...,x n ) enotes the orresponing outome of the gme. The vetor of the hosen prtiulr strtegies, x = (x 1,...,x n ) X 1 X n is lle strtegy profile. In se g is seprle, the seprting funtion g i ssigns n outome to every strtegy of plyer i, n so in this ontext the term ssignility (or property AS in short) is use inste of seprility. The prolem stuie in this pper n e formulte s follows: given n n-person(prtilly efine) gme form (n n-vrite isrete funtion) eie whether it is ssignle (seprle). We shll see in Setion 3 tht this prolem is solvle in polynomil time for n = 2 n tht it is omputtionlly hr for n 3 for the prtilly efine se, n for n 4 for the fully efine se. In those situtions when reognizing ssignility is iffiult, it mkes sense to look for onitions tht (i) re suffiient for ssignility n (ii) n e teste in polynomil time. Suh onitions will e stuie in Setion 2. First, let us rell results relte to the se of two plyers. This se hs een most stuie n est unerstoo so fr. 2

3 1.1 The two-person se Sine ssignility is oviously hereitry property (ny suform of n ssignle gme form, inue y susets of the strtegy sets of the plyers, is oviously lso ssignle), nturl question rises, whether ssignle gme forms mit hrteriztion y finite set of forien suforms. Interestingly, this is not the se lrey for two-person gme forms, s the following infinite fmily (from [2]) of non-ssignle gme forms emonstrtes it: (2) It is not hr to see, tht eh suh gme form is not ssignle, while removl of ny row or olumn mkes it ssignle, i.e. the presente gme forms re miniml non-ssignle. On the other hn, it n e shown [2], tht if ssignility is me more restritive y requiring tht every strtegy profile is overe y extly one of the plyers (either g(x 1,x 2 ) = g 1 (x 1 ) or g(x 1,x 2 ) = g 2 (x 2 ) ut not oth), then suh strong ssignility n e hrterize y finite set of miniml non-ssignle gme forms. Note lso, tht ny two person gme form with t most two outomes is lerly ssignle (ssign one outome to ll rows n the other outome to ll olumns), n hene the gme forms in (2) use the miniml numer of three outomes neee for non-ssignility. This oservtion n e generlize to the se of n plyers: ny n-person gme form with t most n outomes is lerly ssignle, n it is esy to onstrut non-ssignle n-person gme forms with n+1 outomes. Suh onstrution proees s follows. Tke n m m m gme form, n for eh of the first n outomes selet m strtegy profiles (thus we nee nm < m n to hve enough strtegy profiles) suh tht no two of them shre ny oorinte vlue. In the two plyer se these re two permuttion sumtries, in (2) the min igonl with outome n the igonl ove it plus the ottom left orner with outome. Eh of these m-tuples of strtegy profiles requires m ifferent plyer strtegies to over ll m outomes. For eh outome k we nee g i (x j ) = k for m istint pirs (i,j)), n so ltogether they use ll nm ville strtegies g i (x j ). Hene, putting the lst outome n+1 to ny still vnt strtegy profile uses the gme form to e non-ssignle. Let us finlly remrk, tht this onstrution works for ny n n m suh tht nm < m n, ut to mke suh gme form miniml non-ssignle is proly very triky tsk (we o not see ny esy wy how to generlize the onstrution of miniml non-ssignle gme forms to ny n > 2). Severl other properties of two-person gme forms whih re onnete to ssignility were stuie intheliterture. Animportnt propertyis tightness. Wesy tht plyer n gurntee susetb A of outomes, if he/she hs strtegy suh tht no mtter wht strtegy the other plyer hooses, the orresponing outome elongs to B. A two-person gme form g : X 1 X 2 A is lle tight (hs property T, in short) if for ny suset B A either one plyer n gurntee B or the other plyer n gurntee B = A\B. Note tht oth of the ove nnot hppen, while there my e suset B A in non-tight gme form suh tht neither plyer 1 n gurntee B, nor plyer 2 n gurntee B. The importne of tightness stems prtly from the ft tht it is in the two plyer se equivlent to Nsh-solvility of gme form [7, 8, 9], whih is pivotl onept in gme theory. No polynomil time lgorithm for verifying tightness is known, however qusi-polynomil one ws suggeste y Fremn n Khhiyn in [5]. Another property of gme forms relte to tightness is totl tightness. A two-person gme form g : X 1 X 2 A is lle totlly tight (TT) if every 2 2 suform of g (whih is two-imensionl rry in this se) is tight, or equivlently, if it ontins onstnt line (i.e. row or olumn). More preisely, 3

4 let us ll g : X 1 X 2 A to e 2 2 restrition of g if X 1 = {x 1,x 1 } X 1 n X 2 = {x 2,x 2 } X 2 re 2-element susets of X 1 n X 2. Then g is TT if for every 2 2 restrition g of g we hve g (x 1,x 2 ) = g (x 1,x 2), or g (x 1,x 2 ) = g (x 1,x 2 ), or g (x 1,x 2) = g (x 1,x 2 ), or g (x 1,x 2) = g (x 1,x 2). It ws shown in [4] tht totlly tight two-person gme forms re oth tight n ssignle. It is esy to oserve, tht heking whether two-person gme form is TT n e one in polynomil time, s there is only polynomil numer of 2 2 suforms to hek (this esy ft ws oserve e.g. in [1] ). Thus property T T provies simple suffiient onition for the ssignility of two-person gme forms. 1.2 The n-person se for n 3 As we shll rell in Setion 3 tht ssignility of two-person gme forms n e teste in polynomil time. A nturl ie is to try to reue the ssignility of higher imensionl gme forms to the two-imensionl se y onsiering projetions. Let g : X 1... X n A e n-person gme form. An i-th two imensionl projetion g i of g is two person gme form where the set of strtegies of the first plyer onsists of the strtegies of plyer i n the set of strtegies of the seon plyer is the iret prout of ll the strtegies of the remining n 1 plyers. Is it true tht g is ssignle if n only if g i is ssignle for every i? Unfortuntely not. Both implitions fil lrey for n = 3. First we shll show n exmple of three-person gme form whih is not ssignle, ut eh of its three two-imensionl 3 9 projetions is ssignle. The gme form is given in Figure Figure 1: A non-ssignle 3D exmple. Clerly, this three person gme form is not ssignle s there re 1 outomes ut only 9 vlues to e ssigne. On the other hn, eh of the three two-imensionl projetions is 9 3 two-person gme form whih is ssignle y ssigning to ll three olumns n ssigning outomes 1 to 9 to the rows (note tht eh row ontins extly two s n extly one other outome in eh of the three projetions). Seon we shll show n exmple of three-person gme form whih is ssignle, ut none of its three two-imensionl projetions is ssignle. The gme form is given in Figure 2. In this exmple the symol stns for n ritrry outome from the set {,,}. Sine there re only three outomes, the three-person gme form is trivilly ssignle y ssigning outome to plnes orthogonl to iretion 1, outome to plnes orthogonl to iretion 2, n outome to 4

5 Figure 2: Assignle 3D exmple tht hs no 2D ssignle projetion, where symol n e ny of, or. plnes orthogonl to iretion 3. On the other hn, eh of the three 9 3 two-imensionl projetions is gme form tht ontins 2 3 suform [ ] whih is not ssignle, n hene none of the projetions is ssignle (note tht this 2 3 suform is the mile exmple in (1)). Therefore, to otin suffiient onitions for ssignility for n 3 we nee to look t other properties of gme forms, e.g. generliztions of the properties stuie in the two-person se. The onepts of tightness n totl tightness n e extene to the generl n-person se in the following wy. Given n n-person gme form g : X 1... X n A n prtition I = K K of the plyers into two omplementry non-empty olitions, the two-person gme form g K : X K X K A is efine s follows. The strtegies of the first n seon plyers re the elements of the iret prouts X K = i K X i n X K = i K X i, respetively. For x X K n x X K we efine g K (x,x) = g(y) where y origintes from x n x y ontenting them n reorering the oorintes oring to the X 1,...,X n orer. An n-person gme form g is lle tight (respetively, totlly tight) if g K is tight (respetively, totlly tight) for ll non-empty K I. Similrly, we ll g wekly tight (respetively, wekly totlly tight if g K is tight (respetively, totlly tight) for ll K I suh tht K = 1. Note, tht in this se we onsier extly the two-imensionl projetions efine ove in the first prgrph of this susetion. We shll enote these onepts y WT n WTT, respetively. Let us remrk tht, y the ove efinition, T = WT n TT = WTT for n 3. Inee, inthisse fornynon-trivil prtition oneofthetwo olitions ontins onlyoneplyer. Let us lso remrk, tht the ove efine onepts (T, WT, TT, n WTT) n e strightforwrly extene to prtilly efine gme forms y repling ll unefine vlues of the gme form y single extr outome, n y requiring the orresponing property for the resulting fully efine gme form. Let us oserve tht testing whether three-person gme form g is (wekly) totlly tight n e one in polynomil time. In ft, s we shll show in Setion 3, property WTT n e teste in polynomil time (with respet to the size of g) for ny n. Therefore, property WTT my e goo nite for 5

6 property tht we re looking for, property whih n e teste in polynomil time n whih implies ssignility. This les to nother results of this pper, nmely tht wek totl tightness implies ssignility of n-person gme forms (oth prtilly n fully efine) for ll n. The struture of the pper is s follows. In Setion 2 we prove tht WTT AS for every n, tht is, tht property W T T implies ssignility for every n. In Setion 3 we prove tht eiing ssignility of prtilly efine gme forms is NP-omplete for n 3, n tht eiing ssignility of fully efine gme forms is NP-omplete for n 4. We lose the pper y proviing further onnetions to gme theory in Setion 4, n y listing some open prolems in Setion 5. 2 Wek totl tightness implies ssignility When eling with gme forms it is sometimes onvenient to think of the n-person gme form s of n n-imensionl rry n use geometri interprettion for surrys. A line in iretion i is set of strtegy profiles ( 1-imensionl surry) where ll oorintes re fixe n only oorinte i is use s running inex. In gme theoreti terms line in iretion i is 1-imensionl suform otine y fixing the strtegies of ll plyers exept of plyer i. A hyperplne perpeniulr to iretion i is set of strtegy profiles (n (n 1)-imensionl surry) where ll oorintes re use s running inies n only oorinte i is fixe. In gme theoreti terms hyperplne perpeniulr to iretion i is n (n 1)-imensionl suform otine y fixing the strtegy of plyer i. Definition 1 Given gme form g, set S X of strtegy profiles will e lle onstnt region if ll strtegy profiles in S get the sme outome, i.e. if there exists n outome A) suh tht for ll strtegy profiles x S we hve g(x) =. Remrk 2 We will ssume in the reminer of this pper tht the gme form we re eling with ontins no onstnt hyperplne ( hyperplne whih is onstnt region) n no pir of uplite prllel hyperplnes. These ssumptions n e me without loss of generlity s suh hyperplnes oviously influene neither totl tightness nor ssignility of the onsiere gme forms. Let us lso note tht for WTT gme form g the entries in ny 2 2 surry of g {i} (rell tht g {i} is two plyer gme form where plyer i onstitutes one person olition n ll other plyers onstitute the omplementry olition) n e geometrilly thought of s the four intersetions of two ritrry istint lines in iretion i with two ritrry istint (n 1)-imensionl hyperplnes perpeniulr to iretion i. Let us strt with simple lemm esriing forien sustruture for WTT gme forms. Lemm 3 Let g e n n-person gme form, i n ritrry iretion (plyer), n H j,h k e two istint prllel hyperplnes perpeniulr to iretion i. Furthermore let l i,1 i 4 e four lines (not neessrily ll istint) in iretion i interseting H j in strtegy profiles x i j,1 i 4 n interseting H k in strtegy profiles x i k,1 i 4, suh tht (1) g(x 1 j ) g(x2 j ), (2) g(x 3 k ) g(x4 k ), (3) g(x i j ) g(xi k ),1 i 4. Then g is not WTT gme form. 6

7 H j H k g(x 1 j) g(x 1 k) l 1 g(x 2 j) g(x 2 k) l 2 g(x 3 j) g(x 3 k) l 3 g(x 4 j) g(x 4 k) l 4 Figure 3: A forien onfigurtion in WTT gme forms s in Lemm 3. Proof. Using the inequlities (1) n (3) for the quruple (x 1 j,x2 j,x1 k,x2 k ) we either get iret ontrition to the WTT property of g or we get g(x 1 k ) = g(x2 k ) = k for some outome k A. Similrly using (2) n (3) for the quruple (x 3 j,x4 j,x3 k,x4 k ) we either get iret ontrition to the WTT property of g or we get g(x 3 j ) = g(x4 j ) = j for some outome j A. So let us ssume the ltter in oth ses. Now using (1) we get tht one of g(x 1 j ),g(x2 j ) must iffer from j, so let us enote the iffering strtegy profile y x u j, for u {1,2}. Similrly, using (2) we get g(x3 k ) k or g(x 4 k ) k n let us enote the iffering strtegy profile y x v k, for v {3,4}. This ltogether implies tht the quruple (x u j,xv j,xu k,xv k ) ontrits the WTT property of g, see Figure 3. Note tht if some of the lines l i,1 i 4 oinie (of ourse y the ssumptions l 1 must iffer from l 2 n l 3 must iffer from l 4 ), the proof eomes even simpler. If l 1 = l 3 n l 2 = l 4 (or l 1 = l 4 n l 2 = l 3 ) then the four intersetions immeitely give ontrition to the WTT property. If l 1 = l 3 n l 2 l 4 then the proof ove goes through for u = 2 n v = 4. Symmetrilly, if l 1 l 3 n l 2 = l 4 then the proof ove goes through for u = 1 n v = 3. Let us now efine nottion for speil hyperplne prtitions n stte n prove key property of these prtitions. Definition 4 Let g e n n-person gme form n i n ritrry iretion (plyer). Let H j n H k e two istint prllel hyperplnes perpeniulr to iretion i. For n ritrry line l in iretion i let us enote y x l j n xl k the strtegy profiles t the intersetions of line l with hyperplnes H j n H k respetively. We efine prtition of H j into H j = (k) n H j (k) s follows: H j = (k) = {xl j g(xl j ) = g(xl k )} n H j (k) = {xl j g(xl j ) g(xl k )} Lemm 5 Let g e n n-person WTT gme form, i n ritrry iretion (plyer), n H j,h k e two istint prllel hyperplnes perpeniulr to iretion i. Then H j (k) is onstnt region or H k (j) is onstnt region (or oth re onstnt regions). 7

8 Proof. Assume y ontrition tht neither H j (k) nor H k (j) is onstnt region. This mens tht there exist four lines l i,1 i 4 (not neessrily ll istint) in iretion i interseting H j (k) in strtegy profiles x i j,1 i 4 n interseting H k (j) in strtegy profiles xi k,1 i 4 suh tht (1) g(x 1 j ) g(x2 j (2) g(x 3 k ) g(x4 k (3) g(x i j ) g(xi k ) (euse H j (k) ontins two istint outomes), ) (euse H k (j) ontins two istint outomes), ),1 i 4 (y the efinition of H j (k) n H k (j)). The four lines l i,1 i 4 n their intersetions with hyperplnes H j,h k oviously fulfil the ssumptions of Lemm 3 n hene g is not WTT whih is ontrition. Definition 6 Let g e n n-person WTT gme form, i n ritrry iretion (plyer), n H j,h k e two istint prllel hyperplnes perpeniulr to iretion i. If H j (k) is onstnt region for some outome, then we sy tht H j omintes H k y n enote this ft y H j H k. If H j H k n there exists no outome suh tht H k H j then we sy tht H j stritly omintes H k y n write H j = H k. Note tht H j = H k implies tht H k (j) is not onstnt region. Using the just efine nottion, Lemm 5 n the ft tht we hve no two ientil prllel hyperplnes y Remrk 2 implies the following esy orollry. Corollry 7 Let g e n n-person WTT gme form, i n ritrry iretion (plyer), n H j,h k e two istint prllel hyperplnes perpeniulr to iretion i. Then extly one of the following three onitions is true 1. H j = H k for some outome n there exist two istint outomes in H k (j) (whih re oth ifferent from ) 2. H k = H j for some outome n there exist two istint outomes in H j (k) (whih re oth ifferent from ) 3. H j H k n H k H j for some outomes. Remrk 8 It shoul e note tht Corollry 7 gives omplete hrteriztion of WTT gme forms. Nmely, gme form is WTT if n only if ny pir of prllel hyperplnes fulfills extly one of the three properties speifie in Corollry 7. The left to right implitions is prove in Lemm 5 while the reverse implition is trivil. Now we shll show tht hyperplne nnot stritly ominte two other hyperplnes y two ifferent outomes. Lemm 9 Let g e n n-person WTT gme form, i n ritrry iretion (plyer), n H l,h j,h k three istint prllel hyperplnes perpeniulr to iretion i suh tht H l = H j n H l = H k for some outomes n. Then =. 8

9 Proof. Assume y ontrition tht. Then, hyperplne H l n e prtitione into three isjoint regions, nmely the onstnt region H l (j), onstnt region H l (k), n region H= l (j) H= l (k) where the outomes re the sme in ll three hyperplnes for ny perpeniulr line in iretion i. This in prtiulr implies tht H l (j) H= l (k) n H l (k) H= l (j), see Figure 4. Now H l = H j implies tht there exist two istint lines l 1,l 2 in iretion i interseting H l (j) (n thus lso H l = (k)) in profiles x1 l,x2 l for whih g(x1 l ) = g(x2 l ) =, interseting H j (l) in profiles x 1 j,x2 j for whih g(x1 j ) = 1 2 = g(x 2 j ) (here we use the strit omintion), n interseting H= k (l) in profiles x 1 k,x2 k for whih g(x1 k ) = g(x2 k ) =. Note tht the outomes 1, 2, re pirwise istint. Similrly, H l = H k implies tht thereexist two istint lines l 3,l 4 in iretion i interseting H l (k) (n thus lso H l = (j)) in profiles x3 l,x4 l for whih g(x3 l ) = g(x4 l ) =, interseting H= j (l) in profiles x3 j,x4 j for whih g(x 3 j ) = g(x4 j ) =, n interseting H k (l) in profiles x3 k,x4 k for whih g(x3 k ) = 1, g(x 3 k ) = 2 for 1, 2, pirwise istint. H j = H l = H k 1 2 = = l 1 l 2 H l (j) H= l (k) = = 1 2 l 3 l 4 H l (k) H= l (j) Figure 4: Strit ominne y two ifferent outomes les to the forien onfigurtion s in Lemm 3. It is esy to hek tht the four pirwise istint lines l i,1 i 4 n their intersetions with hyperplnes H j,h k fulfil the ssumptions of Lemm 3 n hene g is not WTT whih is ontrition. Lemm 9 llows us to ssoite unique outome to every hyperplne tht stritly omintes t lest one other hyperplne. Definition 1 Let g e n n-person WTT gme form, i n ritrry iretion (plyer), n H j e hyperplne perpeniulr to iretion i. If there exists hyperplne H k prllel to H j n n output suh tht H j = H k then we ll the proper outome of H j. Note tht hyperplne H j tht oes not stritly ominte ny other hyperplne must hve the property (y Corollry 7), tht it is ominte (non-stritly or stritly) y every other hyperplne prllel to H j. We shll ll suh hyperplnefor whih noproperoutome is efinesink hyperplne. 9

10 Definition 11 Let g e n n-person WTT gme form, i n ritrry iretion (plyer), n H j e hyperplne perpeniulr to iretion i. We shll ll H j sink hyperplne if for every hyperplne H k, k j, perpeniulr to iretion i there exists n outome k suh tht H k k Hj. If there exist no sink hyperplne in ny iretion then g is lle no-sink gme form. Definitions 1 n 11 llow us to formulte the following simple orollry. Corollry 12 Let g e n n-person WTT gme form, i n ritrry iretion (plyer), n H j e hyperplne perpeniulr to iretion i. Then either H j hs proper outome or it is sink hyperplne. We will now stuy no-sink WTT gme forms. In suh gme form every hyperplne stritly omintes t lest one of its prllel hyperplnes, whih in turn implies tht there must e yle (or severl yles) of strong ominne reltions mong ll hyperplnes in every iretion. Note tht suh gme forms exist, onsier for instne the following 2-person gme form (3) in whih R 1 = R 3, R 3 = R 2, n R 2 = R 1, where R i is the i-th row of the gme form. The sme yle of strit ominne hols mong the olumns C 1, C 2, n C 3 ue to the symmetry w.r.t. the min igonl. Generting n exmple for three plyers is muh more iffiult to o y hn, ut two suh gme forms were omputer generte using oe of V.Oulov. Eh of them is rry isplye elow s set of three 2-imensionl 3 3 surrys (hyperplnes). The first exmple is shown in Figure 5. = H 1 H 3 = H 2 = Figure 5: First no-sink 3D exmple. Clerly H 1 = H 3, H 3 = H 2, n H 2 = H 1. Note tht H 3 is extly the two imensionl gme form from (3) n the reltions from there rry over to the row hyperplnes R i, 1 i 3 (in 1

11 prtiulr R 1 = R 3, R 3 = R 2, n R 2 = R 1 ) n ue to symmetry lso to the olumn hyperplnes C i, 1 i 3. The seon exmple we hve is shown in Figure 6. Note tht only H 3 is ifferent. We leve the etetion of the three strong ominne yles in ll three iretions to the reer. We onjeture, tht no-sink gme forms exist for ny n, not just for n = 2 n n = 3 s isplye ove. Before we strt to stuy the properties of no-sink WTT gme forms let us introue two efinitions. = H 1 H 3 = H 2 = Figure 6: Seon no-sink 3D exmple. Definition 13 Let g e n n-person WTT gme form, x = (x 1,...,x n ) strtegy profile, n i n ritrry iretion (plyer). Then H x i enotes the hyperplne perpeniulr to iretion i ontining the profile x. Tht is, H x i onsists of ll strtegy profiles in g whih hve the i-th oorinte equl to x i. Definition 14 Let g e n n-person no-sink WTT gme form. We sy tht g ontins k-ox if there exist two strtegy profiles x = (x 1,...,x n ) n y = (y 1,...,y n ) suh tht: 1. g(x) g(y), 2. x n y iffer in extly k oorintes i 1,...,i k (so x n y spn k-imensionl sugme form of 2 k strtegy profiles), n 3. for every 1 j k it hols tht g(x) is not the proper outome of H x i j n g(y) is not the proper outome of H y i j. Now we re rey to stte severl properties of no-sink WTT gme forms. Lemm 15 Let g e n n-person no-sink WTT gme form whih ontins k-ox for some 1 k n. Then g ontins (k 1)-ox or 1-ox. 11

12 Proof. Let us ssume without loss of generlity tht profiles x n y spnning the k-ox iffer in the first k oorintes (if not we n permute the oorintes), i.e. x = (x 1,...,x n ) n y = (y 1,...,y n ) where x i y i for 1 i k n x i = y i for k+1 i n. Now onsier iretion 1 n strtegy profiles x = (y 1,x 2...,x n ) n y = (x 1,y 2...,y n ). Notie tht the pirs x,x n y,y lie on lines in iretion 1 while the pirs x,y n x,y elong to two hyperplnes perpeniulr to iretion 1 (nmely H x 1 n H y 1 ). Thus the retngle x,x,y,y must ontin onstnt line ue to the WTT property. Now we hve four possiilities: 1. if g(x ) = g(y) then x,x spn 1-ox, 2. if g(x ) = g(x) then y,x spn (k 1)-ox, 3. if g(y ) = g(y) then x,y spn (k 1)-ox, n 4. if g(y ) = g(x) then y,y spn 1-ox. In ll four ses the three properties efining the speifie 1-ox or (k 1)-ox follow esily from the properties of the k-ox. In prtiulr, in the first se g(x) g(x ) = g(y), x n x iffer only in the first oorinte, n pir of prllel hyperplnes H1 x n Hx 1 = Hy 1 fulfills the thir property. In the seon se g(y) g(x ) = g(x), y n x iffer in extly (k 1) oorintes (nmely 2,...,k), n (k 1) pirs of prllel hyperplnes H y i n Hi x = Hi x for 2 i k fulfill the thir property. The thir n fourth se re symmetri. Lemm 16 Let g e n n-person no-sink WTT gme form. Then g ontins no 1-ox. Proof. By ontrition let x n y e two profiles spnning 1-ox, i.e. suh tht 1. g(x) g(y), 2. x n y iffer in extly 1 oorinte, i.e. lie on line l in some iretion i, n 3. g(x) is not the proper outome of H x i n g(y) is not the proper outome of H y i. Let us enote the proper outome of H 1 = Hi x y g(x) n the proper outome of H 2 = H y i y g(y). Sine g(x) g(y) we n hve neither H 1 = H 2 nor H 2 = H 1. Consequently, H 1 nnot stritly ominte H 2 y Lemm 9, sine we ssume to e its proper outome. Similrly H 2 g(x) g(y) nnot stritly ominte H 1. Therefore, y Corollry 7 we must hve oth H 1 H 2 n H 2 H 1, see Figure 7. Hene, there must exist thir hyperplne H 3 perpeniulr to iretion i n istint from H 1,H 2 suh tht H 1 = H 3. This implies tht line l intersets H 3 in some strtegy profile z with g(z) = g(x), euse x elongs to H 1 = (3). Moreover, there must exist two istint lines l n l in iretion i interseting H 1 (3) in profiles x n x with g(x ) = g(x ) = n interseting H 3 (1) in profiles z n z where g(z ) =, g(z ) = with,, pirwise istint. One of, must e ifferent from g(x) so let us ssume g(x) = g(z). Let l n l interset H 2 in profiles y n y g(x). Beuse H 1 H 2, lines l n l interset H 1 = (2) n so g(y ) = g(y ) =. Now onsier the retngle y,z,z,y on lines l,l n in hyperplnes H 2,H 3. We hve g(y) g(z) = g(x), g(z) g(z ) =, n = g(z ) g(y ) =. Therefore, WTT property implies g(y) = g(y ) =. However, now the quruple y,y,z,z with g(y ) = g(y ) = g(y) =, g(z ) =, g(z g(y) ) = with,, pirwise istint implies H 2 = H 3 whih ontrits the ft tht the proper outome of H 2 is g(y). Lemm 15 n Lemm 16 of ourse hve n ovious orollry. 12

13 g(x) H 3 = H 1 g(y) H 2 g(z) = g(x) = g(x) g(y) l = g(z ) = g(x ) = = l g(z ) = g(x ) = = l Figure 7: Configurtion showing tht no 1-ox n exists in WTT gme form. Corollry 17 Let g e n n-person no-sink WTT gme form n let k e ritrry, 1 k n. Then g ontins no k-ox. Proof. Let us ssume y ontrition tht g ontins k-ox for some 1 k n. Using Lemm 15 we get tht g ontins (k 1)-ox or 1-ox, the ltter eing impossile ue to Lemm 16. Iterting the rgument we susequently get tht g ontins (k 2)-ox, (k 3)-ox n so on, until we finlly get tht g ontins 1-ox, whih ontrits Lemm 16. Let us introue more terminology onnete to ssignle gme forms. Let g e n ssignle n-person gme form n g i, 1 i n, funtions gurnteeing the ssignility of g. Let x i X i e strtegy of plyer i. If g i (x i ) = for some outome, we sy tht hyperplne H efine y fixing the strtegy of plyer i to x i is ssigne n outome. If hyperplne H is ssigne outome then we sy tht strtegy profile x H is overe y H if g(x) = n not overe y H if g(x). Now let us formulte the finl sttement out no-sink WTT gme forms. Lemm 18 Let g e n n-person no-sink WTT gme form. Then g is ssignle. Proof. Let us ssign to every hyperplne perpeniulr to iretion i, 1 i (n 1), its proper outome (i.e. ll hyperplnes exept of those perpeniulr to iretion n re now ssigne). Let H e hyperplneperpeniulrtoiretion nnlet x,y etwostrtegy profilesinh whihrenotovere y ny hyperplne orthogonl to H, i.e. g(x) is not the proper outome of H x i for ny 1 i (n 1), n g(y) is not the proper outome of H y i for ny 1 i (n 1). If g(x) g(y) then g ontins k-ox for some 1 k (n 1) where k is the numer of oorintes in whih x n y iffer (they nnot iffer in ll n oorintes sine they re oth in H). However, this is impossile in WTT gme 13

14 form ue to Lemm 17, n therefore g(x) = g(y) = for some must hol. Sine x,y were selete s ritrry two not overe strtegy profiles, it follows tht ll not overe profiles in H hve the sme outome n thus n e overe y ssigning to H. The sme n e one for every hyperplne perpeniulr to iretion n n hene g is ssignle. It seems quite intuitive, tht lso every hyperplne perpeniulr to iretion n, whih is ssigne the ommon outome of ll unovere profiles, is in ft ssigne its proper outome. We onjeture tht it is inee the se whih woul mke the sttement of the lgorithm prouing fesile ssignment muh simpler: ssign to eh hyperplne (in ny iretion) its proper outome. However, we urrently hve neither proof of this ft nor ounterexmple, so we leve this s n open reserh question. Now we re finlly rey to stte n prove the min result of this pper. Theorem 19 Let g e n n-person WTT gme form. Then g is ssignle. Proof. We shll proee y inution on n. The se se n = 1 is trivil. In this se g is just single line whih is trivilly WTT (there re no 2 2 sumtries to onsier) n whih is lso esily ssignle (eh strtegy of the single plyer is ssigne the only outome elonging to tht strtegy). Let us ssume for the inution step tht the sttement of the theorem is true for ll (n 1)-person WTT gme forms n let g e n n-person WTT gme form. Now there re two possiilities. Either g hs no sink hyperplne in ny iretion n then it is ssignle y Lemm 18, or there exists iretion i n hyperplne H j perpeniulr to i suh tht H j is sink hyperplne. In the ltter se there exists n outome k suh tht H k k Hj for every hyperplne H k perpeniulr to iretion i, k j. We ssign k to H k for every k j whih overs ll profiles in H k (j) regions of the hyperplnes H k, k j (the omintion H k k Hj implies g(x) = k for every x H k (j)). It remins to over profiles in the H k = (j) regions of the hyperplnes H k, k j, n in hyperplne H j. However, H j is n (n 1)-person WTT gme form whih is ssignle y the inution hypothesis. Moreover, if we exten the ssignment of ll (n 2)-imensionl hyperplnes insie of H j given y the hypothesis to e the sme for the (n 1)-imensionl hyperplnes originting from the (n 2)-imensionl hyperplnes y ing the oorinte i s running inex (extening the (n 2)- imensionl hyperplnes long the lines in iretion i) then this extene ssignment lerly overs ll profiles in the H k = (j) regions of ll hyperplnes H k, k j. Thus ll strtegy profiles in g re overe, whih finishes the proof. Note tht the proof of Theorem 19 gives reursive lgorithm onstruting fesile ssignment for n ritrry n-person WTT gme form. Tht hs n impt on the omplexity of reognizing the WTT property n susequently onstruting fesile ssignment for WTT gme form, s we shll see in the next setion. 3 Complexity of reognition of WTT n AS gme forms First let us relize tht WTT gme forms n e reognize in polynomil time with respet to their size, i.e. with respet to the totl numer of strtegy profiles. Moreover, let us note tht for WTT gme form fesile ssignment n e onstrute in polynomil time s well using the lgorithm impliitly present in the proof of Theorem 19. Theorem 2 Let g e n n-person gme form of size s 1 s 2 s n. Let us enote s = n i=1 s i the sum of sizes in ll iretions n p = n i=1 s i the prout of sizes in ll iretions, i.e. let p e the totl numer of ll strtegy profiles in g. Then it n e teste in O(np 2 ) time whether g is WTT n in the ffirmtive se fesile ssignment for g n e onstrute in O(nsp) time. 14

15 Proof. To test the WTT property, it suffies to test for eh iretion i ll 2 2 surrys efine y hoie of two of the p/s i istint lines in iretion i n two of the s i istint hyperplnes perpeniulr to iretion i. There re O((p/s i ) 2 ) pirs of suh lines n O((s i ) 2 ) pirs of suh hyperplnes. Thus there re O(p 2 ) 2 2 surrys to hek for iretion i n thus ltogether O(np 2 ) surrys for ll iretions (heking eh 2 2 surry tkes of ourse just onstnt time). Now let us ssume tht g is WTT. Given two hyperplnes perpeniulr to iretion i (eh ontining p/s i strtegy profiles) it tkes O(p/s i ) time to etet the ominne reltion etween them. There re O(s 2 i ) suh pirs of hyperplnes n so it tkes O(s ip) time to uil the ominne grph for plyer i. Thus it tkes O(sp) time to uil the ominne grphs for ll plyers. Inseg isno-sinkgmeformthen, followingtheproofoflemm18, ll hyperplnesperpeniulr to iretions other thn n re ssigne their proper outomes (these outomes re ontine in the ominne grphs s ege lels). Given hyperplne H perpeniulr to iretion n n profile x H it tkes O(n) time to hek whether it is overe y one of the n 1 lrey ssigne hyperplnes going through x. Therefore it tkes O(np) time to hek ll profiles n ssign outomes lso to hyperplnes perpeniulr to iretion n whih is ominte y O(sp) time neessry to uil the ominne grphs. In se g ontins sink hyperplne then, following the proof of Theorem 19, reursion is invoke. This reursion hs epth t most n n t eh level the time neee to uil the ominne grphs is of ourse ominte y O(sp). Thus the totl time neee to get fesile ssignment is O(nsp). Oviously, the ove theorem is vli lso for prtilly efine gme forms. Rell tht the WTT property in this se mens tht ll unefine vlues re ll reple y single extr outome n WTT property of this fully efine gme form is require. Thus the omplexity of the reognition prolem is equivlent to the fully efine se. When onstruting fesile ssignment for prtilly efine WTT gme form using the proeure for the fully efine se, some hyperplnes my e ssigne the extr outome. This just mens tht suh hyperplnes re not neee to over the efine outomes in the prtilly efine gme form, n hene eh of these hyperplnes my e ssigne n ritrry outome (inste of the extr outome) in fesile ssignment of the prtilly efine gme form. We hve seen ove tht WTT gme forms (fully or prtilly efine) n e reognize in polynomil time. Wht is the omplexity of reognition for ssignle gme forms? As we showe in the previous setion, the set of ssignle gme forms is superset of WTT gme forms. In ft, it is strit superset of the WTT ones even in the two imensionl se n = 2. See exmples in [4], where the implition AS TT is isprove, or the first exmple in (1) in the introution. The next three susetions show how iffiult it is to reognize this strit superset uner ifferent itionl onitions. 3.1 Complexity of reognition of ssignle gme forms for n=2 One wy to ttk the reognition prolem is to formulte the ssignility of gme form g (oth for the fully efine n prtilly efine ses) s CNF stisfiility prolem. If we introue Boolen vriles y k ij for 1 i n, j X i, n k A, where y k ij = 1 mens g i(j) = k, then the esire CNF onsists of two types of luses. The first type gurntees for every strtegy profile x = (x 1,...,x n ) X 1 X n where g(x) = k tht it is overe y one of the seprting funtions: (y k 1x 1 y k 2x 2... y k nx n ) Note tht the size of eh suh luse (the numer of literls in it) is given y the imension of g (y the numer of plyers), n the numer of suh luses is equl to the numer of profiles for whih g is efine. The seon type of luses then gurntees tht t most one outome from A is ssigne to 15

16 every g i (j), 1 i n, j X i : kl A (y k ij y l ij) These luses re ll qurti (two literls per luse). It is not neessry to require tht extly one outome is ssigne to every g i (j) (requiring t most one outome suffies), euse ny prtilly efine fesile ssignment n e of ourse ritrrily omplete to fully efine one. Note, tht for n = 2 the ove formultion yiels 2-SAT instne (ll luses re qurti), whih immeitely implies tht the ssignility of two-person gme form (prtilly or fully efine) n e reognize (n fesile ssignment onstrute, if it exists) in polynomil time with respet to the size of the gme form. On the other hn, given fully efine n-person gme form g with n 3, the omplexity of reognizing whether g is ssignle is not known. We shll ress this prolem in the rest of this setion. First we shll show tht for prtilly efine gme forms the reognition prolem is NP-omplete for n 3. Then we will moify this proof to show tht for fully efine gme forms the reognition prolem is NP-omplete for n 4, leving the se n = 3 open. 3.2 Complexity of reognition of prtilly efine ssignle gme forms for n 3 In this susetion we show tht reognizing ssignle prtilly efine n-person gme forms is NPomplete lrey for n = 3. This prolem is oviously in NP (for ny n) s heking fesiility of given ssignment n e esily one in polynomil time with respet to the size of the gme form. The hrness prt is prove in the following theorem. Theorem 21 It is NP-hr to reognize, whether given prtilly efine 3-person gme form is ssignle or not. Proof. We will proee y onstruting reution from the known NP-hr prolem 3-SAT, stisfiility of CNFs with extly three literls per luse, where we lso ssume without ny loss of generlity tht no luse ontins two literls of the sme vrile. Let m m Φ = C i = (u i v i w i ) i=1 i=1 e n instne of 3-SAT, i.e. 3-CNF on vriles x 1,...,x n where eh u i, v i, n w i is positive or negtive ourrene of some vrile. We ssoite outomes 1,..., m with the luses C 1,...,C m of Φ n efine prtilly efine 3-person gme form g Φ. It onsists of n m m m ox B, where g Φ (i,i,i) = i, i.e. ox B ontins the outomes 1,..., m on its min igonl n it is unefine everywhere else. Let us enote H 1 1,...,H1 m the hyperplnes perpeniulr to iretion 1, n similrly for iretions 2 n 3. Box B serves s seletor. The ft tht hyperplne H 1 i is ssigne outome i mens tht luse C i is stisfie y literl u i (the literl u i gets vlue true), n similrly for H 2 i n v i n lso H 3 i n w i. Clerly, B y itself is ssignle in mny wys - eh strtegy profile on the min igonl of B n e overe y ny of the three hyperplnes inient with it. However, not every suh ssignment orrespons to truth ssignment to vriles x 1,...,x n s it isregrs the ft tht two istint literls my shre the sme vrile. To estlish one-to-one orresponene etween fesile ssignments of g Φ n stisfying truth ssignments of Φ we will ggets to the ox B whih will gurntee tht: 1. If u i = u j for i j, i.e. oth literls re two ourrenes of the sme vrile with the sme polrity, then either H 1 i is ssigne i n simultneously H 1 j is ssigne j (oth u i n u j re true), or neither H 1 i is ssigne i nor H 1 j is ssigne j (oth u i n u j re flse). Similrly for 16

17 v i = v j n w i = w j. In eh of these three ses we nee to fore the ssigne outomes in the ove esrie wy for pir of prllel hyperplnes. 2. If u i = v j for i j, i.e. oth literls re two ourrenes of the sme vrile with the sme polrity, then either H 1 i is ssigne i n simultneously H 2 j is ssigne j (oth u i n u j re true), or neither H 1 i is ssigne i nor H 2 j is ssigne j (oth u i n u j re flse). Similrly for u i = w j n v i = w j. In eh of these three ses we nee to fore the ssigne outomes in the ove esrie wy for pir of perpeniulr hyperplnes. 3. If u i = u j for i j, i.e. oth literls re two ourrenes of the sme vrile with omplementry polrities, then either H 1 i is ssigne i or H 1 j is ssigne j ut not oth (extly one of u i n u j is true n extly one flse). Similrly for v i = v j n w i = w j. In eh of these three ses we nee to fore the ssigne outomes in the ove esrie wy for pir of prllel hyperplnes. 4. If u i = v j for i j, i.e. oth literls re two ourrenes of the sme vrile with omplementry polrities, then either H 1 i is ssigne i or H 2 j is ssigne j ut not oth (extly one of u i n v j is true n extly one flse). Similrly for u i = w j n v i = w j. In eh of these three ses we nee to fore the ssigne outomes in the ove esrie wy for pir of perpeniulr hyperplnes. Eh suh gget will onstnt numer of eite hyperplnes outsie of ox B, where eite mens tht no two ggets shre ommon e hyperplne (of ourse they my shre hyperplnes inient to ox B). Sine the numer of the ove pir-wise reltions is t most qurti in the size of Φ, the onstrute gme form g Φ hs polynomil size with respet to the size of Φ. Before onstruting the four types of ggets with ove esrie properties, let us onstrut ommon foring omponents of suh ggets (lle foring ues). These re rrys with six istint outomes,,,,e,f rrnge in the eight orners (strtegy profiles) of the rry in one of the two possile wys shown in Figure 8. e e f f Figure 8: Two foring ues: Here we ssume tht,,, e, n f re six istint outomes. Sine they re overe y six plnes, oth opies of must e overe y the front plne, n oth opies of must e overe y the k plne. Consequently, we must hve ssigne to the front n to the k plnes in ll fesile ssignments. In oth foring ues there re six hyperplnes to over ll eight orners with six istint outomes. Thus, oth outomes must e overe y the sme hyperplne n so o oth outomes. Hene, in oth foring ues the front hyperplne is fore to e ssigne n the k hyperplne is fore to e ssigne in ny fesile ssignment. Let us now onstrut the four types of require ggets. 17

18 1. Let u i = u j. Let us two hyperplnes Hl 2 n H2 l+1 perpeniulr to iretion 2 n two hyperplnes Hk 3 n H3 k+1 perpeniulr to iretion 3 for some k,l > m. Let us onsier four istint outomes,,,e not ontine mong 1,..., m, n the rry efine y the intersetions of the four e hyperplnes with Hi 1 n Hi 2 s in Figure 9. H 1 i H 1 j H 2 l j e H 3 k+1 H 2 l+1 i H 3 k Figure 9: Gget 1: This is n instne of one of the foring ues, thus ssignments Hk 3 n Hk+1 3 re implie in ny fesile ssignment. These leve only two possile yli fesile ssignments for the remining four plnes. Either i Hi 1, H2 l, j Hj 1 n e H2 l+1, or H1 i, j Hl 2, e Hj 1 n i Hl+1 2. Consequently, in ll fesile ssignments we hve either oth i Hi 1 n j Hj 1 or we hve neither one, simultneously. This gget is foring ue whih fores Hk 3 to e ssigne n H3 k+1 to e ssigne. Now there re only two wys how the remining four hyperplnes n over the four istint outomes i, j,,e. If Hi 1 is ssigne i, it fores Hl 2 to essigne, whih in turn fores H1 j to e ssigne j, whih finlly fores Hl+1 2 to e ssigne e. On the other hn, if H1 i is ssigne, it fores Hl+1 2 to e ssigne i, whih in turn fores Hj 1 to e ssigne e, whih finlly fores H2 l to e ssigne j. Thus the onstrute gget fulfills extly the require properties. Note lso, tht the hyperplnes Hi 1,H1 j whih re inient to the seletor ox B re fore to e ssigne one of the outomes i, j,,e in every fesile ssignment. 2. Let u i = v j. Let us one hyperplne Hl 1 perpeniulr to iretion 1, one hyperplne H2 p perpeniulr to iretion 2, n two hyperplnes Hk 3 n H3 k+1 perpeniulr to iretion 3 for some k,l,p > m. Let us onsier four istint outomes,,,e not ontine mong 1,..., m n the rry efine y the intersetions of the four e hyperplnes with Hi 1 n Hj 2. We ssign the outomes to this rry s in Figure 1. This is gin foring ue whih fores Hk 3 to e ssigne n H3 k+1 to e ssigne. Clerly, either Hi 1 is ssigne i n Hj 2 is ssigne j or lterntively Hi 1 is ssigne j n Hj 2 is ssigne i (n the outomes n e re similrly overe y Hl 1 n H2 p in one of the two possile wys). Thus the onstrute gget fulfills extly the require properties. Note lso, tht the hyperplnes Hi 1,H2 j whih re inient to the seletor ox B re fore to e ssigne one of the outomes i, j in every fesile ssignment. 3. Let u i = u j. Let us two hyperplnes Hl 1 n H1 l+1 perpeniulr to iretion 1, two hyperplneshp 2 nhp+1 2 perpeniulrtoiretion2, ntwohyperplnesh3 k nh3 k+1 perpeniulr to iretion 3 for some k,l,p > m. Let us ssign six istint outomes,,,,e,f not ontine 18

19 H 1 i H 1 l j H 2 j i e H 3 k+1 H 2 p H 3 k Figure 1: Gget 2: This is n instne of one of the foring ues, thus ssignments Hk 3 n Hk+1 3 re implie in ny fesile ssignment. These leve only two possile fesile ssignments for Hi 1 n Hj 2. We hve either i Hi 1 n j Hj 2, or j Hi 1 n i Hj 2. Consequently, in ll fesile ssignments we hve either oth i Hi 1 n j Hj 2 or we hve neither one, simultneously. mong 1,..., m to the rry efine y the intersetions of the six e hyperplnes in suh wy tht we get foring ue whih fores Hk 3 to e ssigne outome. Let us two more hyperplnes Hp+2 2 n H2 p+3 perpeniulr to iretion 2 n onsier the intersetions of Hk 3 with H1 i, H1 j, H2 p+2, n H2 p+3. Let us ssign outomes to this 2 2 surry s in Figure 11. Sine Hk 3 is fore to e ssigne outome, there re just two wys how to over the four outomes in this 2 2 surry. Either Hi 1 is ssigne i, Hp+3 2 is ssigne e, H1 j is ssigne f, n Hp+2 2 is ssigne j, or lterntively Hi 1 is ssigne e, Hp+2 2 is ssigne i, Hj 1 is ssigne j, n Hp+3 2 is ssigne f. Thus the onstrute gget fulfills extly the require properties. Note lso, tht the hyperplnes Hi 1,H1 j whih re inient to the seletor ox B re fore to e ssigne one of the outomes i, j,e,f in every fesile ssignment. 4. Let u i = v j. Let us two hyperplnes Hl 1 n H1 l+1 perpeniulr to iretion 1, two hyperplneshp 2 nh2 p+1 perpeniulrtoiretion2, ntwohyperplnesh3 k nh3 k+1 perpeniulr to iretion 3 for some k,l,p > m. Let us ssign six istint outomes,,,,e,f not ontine mong 1,..., m to the rry efine y the intersetions of the six e hyperplnes in suh wy, tht we get foring ue whih fores Hk 3 to e ssigne outome. Let us one more hyperplne Hl+2 1 perpeniulr to iretion 1 n one more hyperplne H2 p+2 perpeniulr to iretion 2 n onsier the intersetions of Hk 3 with H1 i, H2 j, H1 l+2, n H2 p+2. Let us ssign outomes to this 2 2 surry s in Figure 12. Sine Hk 3 is fore to e ssigne outome, there re just two wys how to over the four outomes in this 2 2 surry. Either Hi 1 is ssigne i, Hj 2 is ssigne e, H1 l+2 is ssigne j, n Hp+2 2 is ssigne f, or lterntively H1 i is ssigne e, Hp+2 2 is ssigne i, Hl+2 1 is ssigne f, n Hj 2 is ssigne j. Thus the onstrute gget fulfills extly the require properties. Note lso, tht the hyperplnes Hi 1,H2 j whih re inient to the seletor ox B re fore to e ssigne one of the outomes i, j,e,f in every fesile ssignment. It follows from the ove onstrutions tht if there exists fesile ssignment to gme form g Φ then Φ hs stisfying ssignment. Inee, the fesiility of the ssignment for the strtegy profiles on 19

20 H 1 i H 1 j H 2 p+3 i j H 1 l H 1 l+1 H 2 p+2 e f f H 2 p+1 e H 3 k+1 H 2 p H 3 k Figure 11: Gget 3: In this gget we hve foring ue isjoint from the hyperplnes inient with the seletor ox, implying tht is ssigne to Hk 3 in ll fesile ssignments. The intersetion of this hyperplne with four others, s in the piture ove, reues the possile fesile ssignments to the ses when either i is ssigne to Hi 1 or j is ssigne to Hj 1, ut not oth. the min igonl of the seletor ox implies tht eh luse hs stisfying literl, n the fesiility of the ssignment in the strtegy profiles of the ggets imply tht these truth vlues re onsistent. Conversely, if we hve stisfying truth ssignment to Φ, then we n erive fesile ssignment to ll hyperplnes Hi 1, H2 j n H3 k whih over the strtegy profiles long the igonl of the seletor ox, n exten these to over ll other strtegy profiles y the proven properties of the ggets. 3.3 Complexity of reognition of fully efine ssignle gme forms for n 4 In this susetion we will moify the proof of Theorem 21 to show tht the reognition prolem is NP-omplete lso for fully efine gme forms, this time for n 4, leving the se n = 3 open. Theorem 22 It is NP-hr to reognize, whether given fully efine 4-person gme form is ssignle or not. Proof. Let us repet the onstrution from the proof of Theorem 21 with these hnges: All strtegy profiles whih were unefine in the onstrution now get new itionl outome, whih proues fully efine gme form. If the onstrution proue 3-person gme form of size s 1 s 2 s 3 we shll onsier it now s 4-person gme form of size s 1 s 2 s 3 1, n enote the single hyperplne perpeniulr to the e iretion H

21 H 1 i H 1 l+2 H 2 j e j H 1 l+1 H 1 l H 2 p+2 i f f H 2 p+1 e H 3 k+1 H 2 p H 3 k Figure 12: Gget 4: In this gget we hve foring ue isjoint from the hyperplnes inient with the seletor ox, implying tht is ssigne to Hk 3 in ll fesile ssignments. The intersetion of this hyperplne with four others, s in the piture ove, reues the possile fesile ssignments to the ses when either i is ssigne to Hi 1 or j is ssigne to Hj 1, ut not oth. We shll ssume tht the input 3-CNF Φ stisfies the following itionl property: if we elete ny two luses from Φ, the remining 3-CNF ontins some luse C i with non-trivil literl in the first position, luse C j with non-trivil literl in the seon position, n luse C k with non-trivil literl in the thir position, where trivil literl is literl whih represents the only ourrene of its vrile in the entire formul. This ssumption n e me without losing the NP-hrness of the 3-SAT prolem restrite to suh inputs. Let us first note tht we n ssume tht every vrile ppers t lest twie in two ifferent luses of the input 3-CNF. Otherwise, we n fix the vlue of the unique pperne without hnging stisfiility of the input. We lim tht 3-CNF tht oes not stisfy the property lime ove, n tht hs every vrile ppering t lest twie, nnot hve more thn 1 luses. To see this let us ssume tht y eleting the first two luses we hve trivil literl in eh of the remining luses. All of the vriles of these trivil literls must hve then their seon pperne in the elete luses, tht is we nnot hve more thn 6 suh trivil literls. Therefore, if we hve t lest 11 luses, then we must hve three suh tht they o not involve trivil literls in ny positions. Now it is ler tht ny fesile ssignment of outomes to hyperplnes in the proof of Theorem 21 n e extene to fesile ssignment of outomes to hyperplnes for the fully efine gme form y ssigning outome to H1 4. Now we shll show the other iretion, i.e. prove, tht the ssignment of outome to H1 4 is fore, i.e. there is no fesile ssignment of outomes to hyperplnes of the 4-person gme form in whih H1 4 is ssigne something else. This will in turn prove, tht ny fesile ssignment of the fully efine 4-person gme form efines fesile ssignment for the prtilly efine 3-person gme form whih is otine y eleting ll outomes n onsiering the three imensionl 21

CS 491G Combinatorial Optimization Lecture Notes

CS 491G Combinatorial Optimization Lecture Notes CS 491G Comintoril Optimiztion Leture Notes Dvi Owen July 30, August 1 1 Mthings Figure 1: two possile mthings in simple grph. Definition 1 Given grph G = V, E, mthing is olletion of eges M suh tht e i,

More information

Solutions for HW9. Bipartite: put the red vertices in V 1 and the black in V 2. Not bipartite!

Solutions for HW9. Bipartite: put the red vertices in V 1 and the black in V 2. Not bipartite! Solutions for HW9 Exerise 28. () Drw C 6, W 6 K 6, n K 5,3. C 6 : W 6 : K 6 : K 5,3 : () Whih of the following re iprtite? Justify your nswer. Biprtite: put the re verties in V 1 n the lk in V 2. Biprtite:

More information

Counting Paths Between Vertices. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs

Counting Paths Between Vertices. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs. Isomorphism of Graphs Isomorphism of Grphs Definition The simple grphs G 1 = (V 1, E 1 ) n G = (V, E ) re isomorphi if there is ijetion (n oneto-one n onto funtion) f from V 1 to V with the property tht n re jent in G 1 if

More information

18.06 Problem Set 4 Due Wednesday, Oct. 11, 2006 at 4:00 p.m. in 2-106

18.06 Problem Set 4 Due Wednesday, Oct. 11, 2006 at 4:00 p.m. in 2-106 8. Problem Set Due Wenesy, Ot., t : p.m. in - Problem Mony / Consier the eight vetors 5, 5, 5,..., () List ll of the one-element, linerly epenent sets forme from these. (b) Wht re the two-element, linerly

More information

Mid-Term Examination - Spring 2014 Mathematical Programming with Applications to Economics Total Score: 45; Time: 3 hours

Mid-Term Examination - Spring 2014 Mathematical Programming with Applications to Economics Total Score: 45; Time: 3 hours Mi-Term Exmintion - Spring 0 Mthemtil Progrmming with Applitions to Eonomis Totl Sore: 5; Time: hours. Let G = (N, E) e irete grph. Define the inegree of vertex i N s the numer of eges tht re oming into

More information

On a Class of Planar Graphs with Straight-Line Grid Drawings on Linear Area

On a Class of Planar Graphs with Straight-Line Grid Drawings on Linear Area Journl of Grph Algorithms n Applitions http://jg.info/ vol. 13, no. 2, pp. 153 177 (2009) On Clss of Plnr Grphs with Stright-Line Gri Drwings on Liner Are M. Rezul Krim 1,2 M. Siur Rhmn 1 1 Deprtment of

More information

Necessary and sucient conditions for some two. Abstract. Further we show that the necessary conditions for the existence of an OD(44 s 1 s 2 )

Necessary and sucient conditions for some two. Abstract. Further we show that the necessary conditions for the existence of an OD(44 s 1 s 2 ) Neessry n suient onitions for some two vrile orthogonl esigns in orer 44 C. Koukouvinos, M. Mitrouli y, n Jennifer Seerry z Deite to Professor Anne Penfol Street Astrt We give new lgorithm whih llows us

More information

2.4 Theoretical Foundations

2.4 Theoretical Foundations 2 Progrmming Lnguge Syntx 2.4 Theoretil Fountions As note in the min text, snners n prsers re se on the finite utomt n pushown utomt tht form the ottom two levels of the Chomsky lnguge hierrhy. At eh level

More information

Lecture 2: Cayley Graphs

Lecture 2: Cayley Graphs Mth 137B Professor: Pri Brtlett Leture 2: Cyley Grphs Week 3 UCSB 2014 (Relevnt soure mteril: Setion VIII.1 of Bollos s Moern Grph Theory; 3.7 of Gosil n Royle s Algeri Grph Theory; vrious ppers I ve re

More information

1 PYTHAGORAS THEOREM 1. Given a right angled triangle, the square of the hypotenuse is equal to the sum of the squares of the other two sides.

1 PYTHAGORAS THEOREM 1. Given a right angled triangle, the square of the hypotenuse is equal to the sum of the squares of the other two sides. 1 PYTHAGORAS THEOREM 1 1 Pythgors Theorem In this setion we will present geometri proof of the fmous theorem of Pythgors. Given right ngled tringle, the squre of the hypotenuse is equl to the sum of the

More information

Lecture 8: Abstract Algebra

Lecture 8: Abstract Algebra Mth 94 Professor: Pri Brtlett Leture 8: Astrt Alger Week 8 UCSB 2015 This is the eighth week of the Mthemtis Sujet Test GRE prep ourse; here, we run very rough-n-tumle review of strt lger! As lwys, this

More information

Logic, Set Theory and Computability [M. Coppenbarger]

Logic, Set Theory and Computability [M. Coppenbarger] 14 Orer (Hnout) Definition 7-11: A reltion is qusi-orering (or preorer) if it is reflexive n trnsitive. A quisi-orering tht is symmetri is n equivlene reltion. A qusi-orering tht is nti-symmetri is n orer

More information

Arrow s Impossibility Theorem

Arrow s Impossibility Theorem Rep Voting Prdoxes Properties Arrow s Theorem Arrow s Impossiility Theorem Leture 12 Arrow s Impossiility Theorem Leture 12, Slide 1 Rep Voting Prdoxes Properties Arrow s Theorem Leture Overview 1 Rep

More information

If the numbering is a,b,c,d 1,2,3,4, then the matrix representation is as follows:

If the numbering is a,b,c,d 1,2,3,4, then the matrix representation is as follows: Reltions. Solutions 1. ) true; ) true; ) flse; ) true; e) flse; f) true; g) flse; h) true; 2. 2 A B 3. Consier ll reltions tht o not inlue the given pir s n element. Oviously, the rest of the reltions

More information

6.5 Improper integrals

6.5 Improper integrals Eerpt from "Clulus" 3 AoPS In. www.rtofprolemsolving.om 6.5. IMPROPER INTEGRALS 6.5 Improper integrls As we ve seen, we use the definite integrl R f to ompute the re of the region under the grph of y =

More information

Solutions to Problem Set #1

Solutions to Problem Set #1 CSE 233 Spring, 2016 Solutions to Prolem Set #1 1. The movie tse onsists of the following two reltions movie: title, iretor, tor sheule: theter, title The first reltion provies titles, iretors, n tors

More information

Arrow s Impossibility Theorem

Arrow s Impossibility Theorem Rep Fun Gme Properties Arrow s Theorem Arrow s Impossiility Theorem Leture 12 Arrow s Impossiility Theorem Leture 12, Slide 1 Rep Fun Gme Properties Arrow s Theorem Leture Overview 1 Rep 2 Fun Gme 3 Properties

More information

CIT 596 Theory of Computation 1. Graphs and Digraphs

CIT 596 Theory of Computation 1. Graphs and Digraphs CIT 596 Theory of Computtion 1 A grph G = (V (G), E(G)) onsists of two finite sets: V (G), the vertex set of the grph, often enote y just V, whih is nonempty set of elements lle verties, n E(G), the ege

More information

Project 6: Minigoals Towards Simplifying and Rewriting Expressions

Project 6: Minigoals Towards Simplifying and Rewriting Expressions MAT 51 Wldis Projet 6: Minigols Towrds Simplifying nd Rewriting Expressions The distriutive property nd like terms You hve proly lerned in previous lsses out dding like terms ut one prolem with the wy

More information

Factorising FACTORISING.

Factorising FACTORISING. Ftorising FACTORISING www.mthletis.om.u Ftorising FACTORISING Ftorising is the opposite of expning. It is the proess of putting expressions into rkets rther thn expning them out. In this setion you will

More information

The DOACROSS statement

The DOACROSS statement The DOACROSS sttement Is prllel loop similr to DOALL, ut it llows prouer-onsumer type of synhroniztion. Synhroniztion is llowe from lower to higher itertions sine it is ssume tht lower itertions re selete

More information

Maximum size of a minimum watching system and the graphs achieving the bound

Maximum size of a minimum watching system and the graphs achieving the bound Mximum size of minimum wthing system n the grphs hieving the oun Tille mximum un système e ontrôle minimum et les grphes tteignnt l orne Dvi Auger Irène Chron Olivier Hury Antoine Lostein 00D0 Mrs 00 Déprtement

More information

arxiv: v2 [math.co] 31 Oct 2016

arxiv: v2 [math.co] 31 Oct 2016 On exlue minors of onnetivity 2 for the lss of frme mtrois rxiv:1502.06896v2 [mth.co] 31 Ot 2016 Mtt DeVos Dryl Funk Irene Pivotto Astrt We investigte the set of exlue minors of onnetivity 2 for the lss

More information

On the existence of a cherry-picking sequence

On the existence of a cherry-picking sequence On the existene of herry-piking sequene Jnosh Döker, Simone Linz Deprtment of Computer Siene, University of Tüingen, Germny Deprtment of Computer Siene, University of Aukln, New Zeln Astrt Reently, the

More information

22: Union Find. CS 473u - Algorithms - Spring April 14, We want to maintain a collection of sets, under the operations of:

22: Union Find. CS 473u - Algorithms - Spring April 14, We want to maintain a collection of sets, under the operations of: 22: Union Fin CS 473u - Algorithms - Spring 2005 April 14, 2005 1 Union-Fin We wnt to mintin olletion of sets, uner the opertions of: 1. MkeSet(x) - rete set tht ontins the single element x. 2. Fin(x)

More information

Discrete Structures Lecture 11

Discrete Structures Lecture 11 Introdution Good morning. In this setion we study funtions. A funtion is mpping from one set to nother set or, perhps, from one set to itself. We study the properties of funtions. A mpping my not e funtion.

More information

Part 4. Integration (with Proofs)

Part 4. Integration (with Proofs) Prt 4. Integrtion (with Proofs) 4.1 Definition Definition A prtition P of [, b] is finite set of points {x 0, x 1,..., x n } with = x 0 < x 1

More information

Lecture 4: Graph Theory and the Four-Color Theorem

Lecture 4: Graph Theory and the Four-Color Theorem CCS Disrete II Professor: Pri Brtlett Leture 4: Grph Theory n the Four-Color Theorem Week 4 UCSB 2015 Through the rest of this lss, we re going to refer frequently to things lle grphs! If you hen t seen

More information

Lecture 6: Coding theory

Lecture 6: Coding theory Leture 6: Coing theory Biology 429 Crl Bergstrom Ferury 4, 2008 Soures: This leture loosely follows Cover n Thoms Chpter 5 n Yeung Chpter 3. As usul, some of the text n equtions re tken iretly from those

More information

Eigenvectors and Eigenvalues

Eigenvectors and Eigenvalues MTB 050 1 ORIGIN 1 Eigenvets n Eigenvlues This wksheet esries the lger use to lulte "prinipl" "hrteristi" iretions lle Eigenvets n the "prinipl" "hrteristi" vlues lle Eigenvlues ssoite with these iretions.

More information

F / x everywhere in some domain containing R. Then, + ). (10.4.1)

F / x everywhere in some domain containing R. Then, + ). (10.4.1) 0.4 Green's theorem in the plne Double integrls over plne region my be trnsforme into line integrls over the bounry of the region n onversely. This is of prtil interest beuse it my simplify the evlution

More information

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution Tehnishe Universität Münhen Winter term 29/ I7 Prof. J. Esprz / J. Křetínský / M. Luttenerger. Ferur 2 Solution Automt nd Forml Lnguges Homework 2 Due 5..29. Exerise 2. Let A e the following finite utomton:

More information

Algebra 2 Semester 1 Practice Final

Algebra 2 Semester 1 Practice Final Alger 2 Semester Prtie Finl Multiple Choie Ientify the hoie tht est ompletes the sttement or nswers the question. To whih set of numers oes the numer elong?. 2 5 integers rtionl numers irrtionl numers

More information

Monochromatic Plane Matchings in Bicolored Point Set

Monochromatic Plane Matchings in Bicolored Point Set CCCG 2017, Ottw, Ontrio, July 26 28, 2017 Monohromti Plne Mthings in Biolore Point Set A. Krim Au-Affsh Sujoy Bhore Pz Crmi Astrt Motivte y networks interply, we stuy the prolem of omputing monohromti

More information

for all x in [a,b], then the area of the region bounded by the graphs of f and g and the vertical lines x = a and x = b is b [ ( ) ( )] A= f x g x dx

for all x in [a,b], then the area of the region bounded by the graphs of f and g and the vertical lines x = a and x = b is b [ ( ) ( )] A= f x g x dx Applitions of Integrtion Are of Region Between Two Curves Ojetive: Fin the re of region etween two urves using integrtion. Fin the re of region etween interseting urves using integrtion. Desrie integrtion

More information

Numbers and indices. 1.1 Fractions. GCSE C Example 1. Handy hint. Key point

Numbers and indices. 1.1 Fractions. GCSE C Example 1. Handy hint. Key point GCSE C Emple 7 Work out 9 Give your nswer in its simplest form Numers n inies Reiprote mens invert or turn upsie own The reiprol of is 9 9 Mke sure you only invert the frtion you re iviing y 7 You multiply

More information

CS 2204 DIGITAL LOGIC & STATE MACHINE DESIGN SPRING 2014

CS 2204 DIGITAL LOGIC & STATE MACHINE DESIGN SPRING 2014 S 224 DIGITAL LOGI & STATE MAHINE DESIGN SPRING 214 DUE : Mrh 27, 214 HOMEWORK III READ : Relte portions of hpters VII n VIII ASSIGNMENT : There re three questions. Solve ll homework n exm prolems s shown

More information

Graph Theory. Simple Graph G = (V, E). V={a,b,c,d,e,f,g,h,k} E={(a,b),(a,g),( a,h),(a,k),(b,c),(b,k),...,(h,k)}

Graph Theory. Simple Graph G = (V, E). V={a,b,c,d,e,f,g,h,k} E={(a,b),(a,g),( a,h),(a,k),(b,c),(b,k),...,(h,k)} Grph Theory Simple Grph G = (V, E). V ={verties}, E={eges}. h k g f e V={,,,,e,f,g,h,k} E={(,),(,g),(,h),(,k),(,),(,k),...,(h,k)} E =16. 1 Grph or Multi-Grph We llow loops n multiple eges. G = (V, E.ψ)

More information

CS311 Computational Structures Regular Languages and Regular Grammars. Lecture 6

CS311 Computational Structures Regular Languages and Regular Grammars. Lecture 6 CS311 Computtionl Strutures Regulr Lnguges nd Regulr Grmmrs Leture 6 1 Wht we know so fr: RLs re losed under produt, union nd * Every RL n e written s RE, nd every RE represents RL Every RL n e reognized

More information

Logic Synthesis and Verification

Logic Synthesis and Verification Logi Synthesis nd Verifition SOPs nd Inompletely Speified Funtions Jie-Hong Rolnd Jing 江介宏 Deprtment of Eletril Engineering Ntionl Tiwn University Fll 2010 Reding: Logi Synthesis in Nutshell Setion 2 most

More information

Now we must transform the original model so we can use the new parameters. = S max. Recruits

Now we must transform the original model so we can use the new parameters. = S max. Recruits MODEL FOR VARIABLE RECRUITMENT (ontinue) Alterntive Prmeteriztions of the pwner-reruit Moels We n write ny moel in numerous ifferent ut equivlent forms. Uner ertin irumstnes it is onvenient to work with

More information

The vertex leafage of chordal graphs

The vertex leafage of chordal graphs The vertex lefge of horl grphs Steven Chplik, Jurj Stho b Deprtment of Physis n Computer Siene, Wilfri Lurier University, 75 University Ave. West, Wterloo, Ontrio N2L 3C5, Cn b DIMAP n Mthemtis Institute,

More information

Surds and Indices. Surds and Indices. Curriculum Ready ACMNA: 233,

Surds and Indices. Surds and Indices. Curriculum Ready ACMNA: 233, Surs n Inies Surs n Inies Curriulum Rey ACMNA:, 6 www.mthletis.om Surs SURDS & & Inies INDICES Inies n surs re very losely relte. A numer uner (squre root sign) is lle sur if the squre root n t e simplifie.

More information

Compression of Palindromes and Regularity.

Compression of Palindromes and Regularity. Compression of Plinromes n Regulrity. Kyoko Shikishim-Tsuji Center for Lierl Arts Eution n Reserh Tenri University 1 Introution In [1], property of likstrem t t view of tse is isusse n it is shown tht

More information

September 30, :24 WSPC/Guidelines Y4Spanner

September 30, :24 WSPC/Guidelines Y4Spanner Septemer 30, 2011 12:24 WSPC/Guielines Y4Spnner Interntionl Journl of Computtionl Geometry & Applitions Worl Sientifi Pulishing Compny π/2-angle YAO GAPHS AE SPANNES POSENJIT BOSE Shool of Computer Siene,

More information

POSITIVE IMPLICATIVE AND ASSOCIATIVE FILTERS OF LATTICE IMPLICATION ALGEBRAS

POSITIVE IMPLICATIVE AND ASSOCIATIVE FILTERS OF LATTICE IMPLICATION ALGEBRAS Bull. Koren Mth. So. 35 (998), No., pp. 53 6 POSITIVE IMPLICATIVE AND ASSOCIATIVE FILTERS OF LATTICE IMPLICATION ALGEBRAS YOUNG BAE JUN*, YANG XU AND KEYUN QIN ABSTRACT. We introue the onepts of positive

More information

A Lower Bound for the Length of a Partial Transversal in a Latin Square, Revised Version

A Lower Bound for the Length of a Partial Transversal in a Latin Square, Revised Version A Lower Bound for the Length of Prtil Trnsversl in Ltin Squre, Revised Version Pooy Htmi nd Peter W. Shor Deprtment of Mthemtil Sienes, Shrif University of Tehnology, P.O.Bo 11365-9415, Tehrn, Irn Deprtment

More information

CARLETON UNIVERSITY. 1.0 Problems and Most Solutions, Sect B, 2005

CARLETON UNIVERSITY. 1.0 Problems and Most Solutions, Sect B, 2005 RLETON UNIVERSIT eprtment of Eletronis ELE 2607 Swithing iruits erury 28, 05; 0 pm.0 Prolems n Most Solutions, Set, 2005 Jn. 2, #8 n #0; Simplify, Prove Prolem. #8 Simplify + + + Reue to four letters (literls).

More information

On the Spectra of Bipartite Directed Subgraphs of K 4

On the Spectra of Bipartite Directed Subgraphs of K 4 On the Spetr of Biprtite Direte Sugrphs of K 4 R. C. Bunge, 1 S. I. El-Znti, 1, H. J. Fry, 1 K. S. Kruss, 2 D. P. Roerts, 3 C. A. Sullivn, 4 A. A. Unsiker, 5 N. E. Witt 6 1 Illinois Stte University, Norml,

More information

Chapter 4 State-Space Planning

Chapter 4 State-Space Planning Leture slides for Automted Plnning: Theory nd Prtie Chpter 4 Stte-Spe Plnning Dn S. Nu CMSC 722, AI Plnning University of Mrylnd, Spring 2008 1 Motivtion Nerly ll plnning proedures re serh proedures Different

More information

Section 2.3. Matrix Inverses

Section 2.3. Matrix Inverses Mtri lger Mtri nverses Setion.. Mtri nverses hree si opertions on mtries, ition, multiplition, n sutrtion, re nlogues for mtries of the sme opertions for numers. n this setion we introue the mtri nlogue

More information

I 3 2 = I I 4 = 2A

I 3 2 = I I 4 = 2A ECE 210 Eletril Ciruit Anlysis University of llinois t Chigo 2.13 We re ske to use KCL to fin urrents 1 4. The key point in pplying KCL in this prolem is to strt with noe where only one of the urrents

More information

Proportions: A ratio is the quotient of two numbers. For example, 2 3

Proportions: A ratio is the quotient of two numbers. For example, 2 3 Proportions: rtio is the quotient of two numers. For exmple, 2 3 is rtio of 2 n 3. n equlity of two rtios is proportion. For exmple, 3 7 = 15 is proportion. 45 If two sets of numers (none of whih is 0)

More information

Lecture 11 Binary Decision Diagrams (BDDs)

Lecture 11 Binary Decision Diagrams (BDDs) C 474A/57A Computer-Aie Logi Design Leture Binry Deision Digrms (BDDs) C 474/575 Susn Lyseky o 3 Boolen Logi untions Representtions untion n e represente in ierent wys ruth tle, eqution, K-mp, iruit, et

More information

Bisimulation, Games & Hennessy Milner logic

Bisimulation, Games & Hennessy Milner logic Bisimultion, Gmes & Hennessy Milner logi Leture 1 of Modelli Mtemtii dei Proessi Conorrenti Pweł Soboiński Univeristy of Southmpton, UK Bisimultion, Gmes & Hennessy Milner logi p.1/32 Clssil lnguge theory

More information

SEMI-EXCIRCLE OF QUADRILATERAL

SEMI-EXCIRCLE OF QUADRILATERAL JP Journl of Mthemtil Sienes Volume 5, Issue &, 05, Pges - 05 Ishn Pulishing House This pper is ville online t http://wwwiphsiom SEMI-EXCIRCLE OF QUADRILATERAL MASHADI, SRI GEMAWATI, HASRIATI AND HESY

More information

Let s divide up the interval [ ab, ] into n subintervals with the same length, so we have

Let s divide up the interval [ ab, ] into n subintervals with the same length, so we have III. INTEGRATION Eonomists seem muh more intereste in mrginl effets n ifferentition thn in integrtion. Integrtion is importnt for fining the epete vlue n vrine of rnom vriles, whih is use in eonometris

More information

Obstructions to chordal circular-arc graphs of small independence number

Obstructions to chordal circular-arc graphs of small independence number Ostrutions to horl irulr-r grphs of smll inepenene numer Mthew Frnis,1 Pvol Hell,2 Jurj Stho,3 Institute of Mth. Sienes, IV Cross Ro, Trmni, Chenni 600 113, Ini Shool of Comp. Siene, Simon Frser University,

More information

MAT 403 NOTES 4. f + f =

MAT 403 NOTES 4. f + f = MAT 403 NOTES 4 1. Fundmentl Theorem o Clulus We will proo more generl version o the FTC thn the textook. But just like the textook, we strt with the ollowing proposition. Let R[, ] e the set o Riemnn

More information

QUADRATIC EQUATION. Contents

QUADRATIC EQUATION. Contents QUADRATIC EQUATION Contents Topi Pge No. Theory 0-04 Exerise - 05-09 Exerise - 09-3 Exerise - 3 4-5 Exerise - 4 6 Answer Key 7-8 Syllus Qudrti equtions with rel oeffiients, reltions etween roots nd oeffiients,

More information

A Primer on Continuous-time Economic Dynamics

A Primer on Continuous-time Economic Dynamics Eonomis 205A Fll 2008 K Kletzer A Primer on Continuous-time Eonomi Dnmis A Liner Differentil Eqution Sstems (i) Simplest se We egin with the simple liner first-orer ifferentil eqution The generl solution

More information

Bases for Vector Spaces

Bases for Vector Spaces Bses for Vector Spces 2-26-25 A set is independent if, roughly speking, there is no redundncy in the set: You cn t uild ny vector in the set s liner comintion of the others A set spns if you cn uild everything

More information

p-adic Egyptian Fractions

p-adic Egyptian Fractions p-adic Egyptin Frctions Contents 1 Introduction 1 2 Trditionl Egyptin Frctions nd Greedy Algorithm 2 3 Set-up 3 4 p-greedy Algorithm 5 5 p-egyptin Trditionl 10 6 Conclusion 1 Introduction An Egyptin frction

More information

CS261: A Second Course in Algorithms Lecture #5: Minimum-Cost Bipartite Matching

CS261: A Second Course in Algorithms Lecture #5: Minimum-Cost Bipartite Matching CS261: A Seon Course in Algorithms Leture #5: Minimum-Cost Biprtite Mthing Tim Roughgren Jnury 19, 2016 1 Preliminries Figure 1: Exmple of iprtite grph. The eges {, } n {, } onstitute mthing. Lst leture

More information

arxiv: v1 [cs.dm] 24 Jul 2017

arxiv: v1 [cs.dm] 24 Jul 2017 Some lsses of grphs tht re not PCGs 1 rxiv:1707.07436v1 [s.dm] 24 Jul 2017 Pierluigi Biohi Angelo Monti Tizin Clmoneri Rossell Petreshi Computer Siene Deprtment, Spienz University of Rome, Itly pierluigi.iohi@gmil.om,

More information

Chapter 3. Vector Spaces. 3.1 Images and Image Arithmetic

Chapter 3. Vector Spaces. 3.1 Images and Image Arithmetic Chpter 3 Vetor Spes In Chpter 2, we sw tht the set of imges possessed numer of onvenient properties. It turns out tht ny set tht possesses similr onvenient properties n e nlyzed in similr wy. In liner

More information

GRUPOS NANTEL BERGERON

GRUPOS NANTEL BERGERON Drft of Septemer 8, 2017 GRUPOS NANTEL BERGERON Astrt. 1. Quik Introution In this mini ourse we will see how to ount severl ttriute relte to symmetries of n ojet. For exmple, how mny ifferent ies with

More information

Total score: /100 points

Total score: /100 points Points misse: Stuent's Nme: Totl sore: /100 points Est Tennessee Stte University Deprtment of Computer n Informtion Sienes CSCI 2710 (Trnoff) Disrete Strutures TEST 2 for Fll Semester, 2004 Re this efore

More information

Automata and Regular Languages

Automata and Regular Languages Chpter 9 Automt n Regulr Lnguges 9. Introution This hpter looks t mthemtil moels of omputtion n lnguges tht esrie them. The moel-lnguge reltionship hs multiple levels. We shll explore the simplest level,

More information

CSE 332. Sorting. Data Abstractions. CSE 332: Data Abstractions. QuickSort Cutoff 1. Where We Are 2. Bounding The MAXIMUM Problem 4

CSE 332. Sorting. Data Abstractions. CSE 332: Data Abstractions. QuickSort Cutoff 1. Where We Are 2. Bounding The MAXIMUM Problem 4 Am Blnk Leture 13 Winter 2016 CSE 332 CSE 332: Dt Astrtions Sorting Dt Astrtions QuikSort Cutoff 1 Where We Are 2 For smll n, the reursion is wste. The onstnts on quik/merge sort re higher thn the ones

More information

CS 573 Automata Theory and Formal Languages

CS 573 Automata Theory and Formal Languages Non-determinism Automt Theory nd Forml Lnguges Professor Leslie Lnder Leture # 3 Septemer 6, 2 To hieve our gol, we need the onept of Non-deterministi Finite Automton with -moves (NFA) An NFA is tuple

More information

Nondeterministic Automata vs Deterministic Automata

Nondeterministic Automata vs Deterministic Automata Nondeterministi Automt vs Deterministi Automt We lerned tht NFA is onvenient model for showing the reltionships mong regulr grmmrs, FA, nd regulr expressions, nd designing them. However, we know tht n

More information

Matrices SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics (c) 1. Definition of a Matrix

Matrices SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics (c) 1. Definition of a Matrix tries Definition of tri mtri is regulr rry of numers enlosed inside rkets SCHOOL OF ENGINEERING & UIL ENVIRONEN Emple he following re ll mtries: ), ) 9, themtis ), d) tries Definition of tri Size of tri

More information

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER MACHINES AND THEIR LANGUAGES ANSWERS

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER MACHINES AND THEIR LANGUAGES ANSWERS The University of ottinghm SCHOOL OF COMPUTR SCIC A LVL 2 MODUL, SPRIG SMSTR 2015 2016 MACHIS AD THIR LAGUAGS ASWRS Time llowed TWO hours Cndidtes my omplete the front over of their nswer ook nd sign their

More information

Farey Fractions. Rickard Fernström. U.U.D.M. Project Report 2017:24. Department of Mathematics Uppsala University

Farey Fractions. Rickard Fernström. U.U.D.M. Project Report 2017:24. Department of Mathematics Uppsala University U.U.D.M. Project Report 07:4 Frey Frctions Rickrd Fernström Exmensrete i mtemtik, 5 hp Hledre: Andres Strömergsson Exmintor: Jörgen Östensson Juni 07 Deprtment of Mthemtics Uppsl University Frey Frctions

More information

Probability. b a b. a b 32.

Probability. b a b. a b 32. Proility If n event n hppen in '' wys nd fil in '' wys, nd eh of these wys is eqully likely, then proility or the hne, or its hppening is, nd tht of its filing is eg, If in lottery there re prizes nd lnks,

More information

(a) A partition P of [a, b] is a finite subset of [a, b] containing a and b. If Q is another partition and P Q, then Q is a refinement of P.

(a) A partition P of [a, b] is a finite subset of [a, b] containing a and b. If Q is another partition and P Q, then Q is a refinement of P. Chpter 7: The Riemnn Integrl When the derivtive is introdued, it is not hrd to see tht the it of the differene quotient should be equl to the slope of the tngent line, or when the horizontl xis is time

More information

SOME INTEGRAL INEQUALITIES FOR HARMONICALLY CONVEX STOCHASTIC PROCESSES ON THE CO-ORDINATES

SOME INTEGRAL INEQUALITIES FOR HARMONICALLY CONVEX STOCHASTIC PROCESSES ON THE CO-ORDINATES Avne Mth Moels & Applitions Vol3 No 8 pp63-75 SOME INTEGRAL INEQUALITIES FOR HARMONICALLY CONVE STOCHASTIC PROCESSES ON THE CO-ORDINATES Nurgül Okur * Imt Işn Yusuf Ust 3 3 Giresun University Deprtment

More information

April 8, 2017 Math 9. Geometry. Solving vector problems. Problem. Prove that if vectors and satisfy, then.

April 8, 2017 Math 9. Geometry. Solving vector problems. Problem. Prove that if vectors and satisfy, then. pril 8, 2017 Mth 9 Geometry Solving vetor prolems Prolem Prove tht if vetors nd stisfy, then Solution 1 onsider the vetor ddition prllelogrm shown in the Figure Sine its digonls hve equl length,, the prllelogrm

More information

New and Improved Spanning Ratios for Yao Graphs

New and Improved Spanning Ratios for Yao Graphs New n Improve Spnning Rtios for Yo Grphs Luis Br Déprtement Informtique Université Lire e Bruxelles lrfl@ul..e Rolf Fgererg Deprtment of Computer Siene University of Southern Denmrk rolf@im.su.k Anré vn

More information

Logic Synthesis and Verification

Logic Synthesis and Verification Logi Synthesis nd Verifition SOPs nd Inompletely Speified Funtions Jie-Hong Rolnd Jing 江介宏 Deprtment of Eletril Engineering Ntionl Tiwn University Fll 22 Reding: Logi Synthesis in Nutshell Setion 2 most

More information

Solids of Revolution

Solids of Revolution Solis of Revolution Solis of revolution re rete tking n re n revolving it roun n is of rottion. There re two methos to etermine the volume of the soli of revolution: the isk metho n the shell metho. Disk

More information

T b a(f) [f ] +. P b a(f) = Conclude that if f is in AC then it is the difference of two monotone absolutely continuous functions.

T b a(f) [f ] +. P b a(f) = Conclude that if f is in AC then it is the difference of two monotone absolutely continuous functions. Rel Vribles, Fll 2014 Problem set 5 Solution suggestions Exerise 1. Let f be bsolutely ontinuous on [, b] Show tht nd T b (f) P b (f) f (x) dx [f ] +. Conlude tht if f is in AC then it is the differene

More information

Maximizing Maximal Angles for Plane Straight-Line Graphs

Maximizing Maximal Angles for Plane Straight-Line Graphs Mximizing Mximl Angles for Plne Stright-Line Grphs Oswin Aihholzer 1, Thoms Hkl 1, Mihel Hoffmnn 2, Clemens Huemer 3, Attil Pór 4, Frniso Sntos 5, Bettin Spekmnn 6, n Birgit Vogtenhuer 1 1 Institute for

More information

where the box contains a finite number of gates from the given collection. Examples of gates that are commonly used are the following: a b

where the box contains a finite number of gates from the given collection. Examples of gates that are commonly used are the following: a b CS 294-2 9/11/04 Quntum Ciruit Model, Solovy-Kitev Theorem, BQP Fll 2004 Leture 4 1 Quntum Ciruit Model 1.1 Clssil Ciruits - Universl Gte Sets A lssil iruit implements multi-output oolen funtion f : {0,1}

More information

Introduction to Olympiad Inequalities

Introduction to Olympiad Inequalities Introdution to Olympid Inequlities Edutionl Studies Progrm HSSP Msshusetts Institute of Tehnology Snj Simonovikj Spring 207 Contents Wrm up nd Am-Gm inequlity 2. Elementry inequlities......................

More information

Data Structures LECTURE 10. Huffman coding. Example. Coding: problem definition

Data Structures LECTURE 10. Huffman coding. Example. Coding: problem definition Dt Strutures, Spring 24 L. Joskowiz Dt Strutures LEURE Humn oing Motivtion Uniquel eipherle oes Prei oes Humn oe onstrution Etensions n pplitions hpter 6.3 pp 385 392 in tetook Motivtion Suppose we wnt

More information

Tutorial Worksheet. 1. Find all solutions to the linear system by following the given steps. x + 2y + 3z = 2 2x + 3y + z = 4.

Tutorial Worksheet. 1. Find all solutions to the linear system by following the given steps. x + 2y + 3z = 2 2x + 3y + z = 4. Mth 5 Tutoril Week 1 - Jnury 1 1 Nme Setion Tutoril Worksheet 1. Find ll solutions to the liner system by following the given steps x + y + z = x + y + z = 4. y + z = Step 1. Write down the rgumented mtrix

More information

Linear Algebra Introduction

Linear Algebra Introduction Introdution Wht is Liner Alger out? Liner Alger is rnh of mthemtis whih emerged yers k nd ws one of the pioneer rnhes of mthemtis Though, initilly it strted with solving of the simple liner eqution x +

More information

CSC2542 State-Space Planning

CSC2542 State-Space Planning CSC2542 Stte-Spe Plnning Sheil MIlrith Deprtment of Computer Siene University of Toronto Fll 2010 1 Aknowlegements Some the slies use in this ourse re moifitions of Dn Nu s leture slies for the textook

More information

EXTENSION OF THE GCD STAR OF DAVID THEOREM TO MORE THAN TWO GCDS CALVIN LONG AND EDWARD KORNTVED

EXTENSION OF THE GCD STAR OF DAVID THEOREM TO MORE THAN TWO GCDS CALVIN LONG AND EDWARD KORNTVED EXTENSION OF THE GCD STAR OF DAVID THEOREM TO MORE THAN TWO GCDS CALVIN LONG AND EDWARD KORNTVED Astrt. The GCD Str of Dvi Theorem n the numerous ppers relte to it hve lrgel een evote to shoing the equlit

More information

Geodesics on Regular Polyhedra with Endpoints at the Vertices

Geodesics on Regular Polyhedra with Endpoints at the Vertices Arnol Mth J (2016) 2:201 211 DOI 101007/s40598-016-0040-z RESEARCH CONTRIBUTION Geoesis on Regulr Polyher with Enpoints t the Verties Dmitry Fuhs 1 To Sergei Thnikov on the osion of his 60th irthy Reeive:

More information

Implication Graphs and Logic Testing

Implication Graphs and Logic Testing Implition Grphs n Logi Testing Vishwni D. Agrwl Jmes J. Dnher Professor Dept. of ECE, Auurn University Auurn, AL 36849 vgrwl@eng.uurn.eu www.eng.uurn.eu/~vgrwl Joint reserh with: K. K. Dve, ATI Reserh,

More information

NON-DETERMINISTIC FSA

NON-DETERMINISTIC FSA Tw o types of non-determinism: NON-DETERMINISTIC FS () Multiple strt-sttes; strt-sttes S Q. The lnguge L(M) ={x:x tkes M from some strt-stte to some finl-stte nd ll of x is proessed}. The string x = is

More information

Outline Data Structures and Algorithms. Data compression. Data compression. Lossy vs. Lossless. Data Compression

Outline Data Structures and Algorithms. Data compression. Data compression. Lossy vs. Lossless. Data Compression 5-2 Dt Strutures n Algorithms Dt Compression n Huffmn s Algorithm th Fe 2003 Rjshekr Rey Outline Dt ompression Lossy n lossless Exmples Forml view Coes Definition Fixe length vs. vrile length Huffmn s

More information

Algorithms & Data Structures Homework 8 HS 18 Exercise Class (Room & TA): Submitted by: Peer Feedback by: Points:

Algorithms & Data Structures Homework 8 HS 18 Exercise Class (Room & TA): Submitted by: Peer Feedback by: Points: Eidgenössishe Tehnishe Hohshule Zürih Eole polytehnique fédérle de Zurih Politenio federle di Zurigo Federl Institute of Tehnology t Zurih Deprtement of Computer Siene. Novemer 0 Mrkus Püshel, Dvid Steurer

More information

System Validation (IN4387) November 2, 2012, 14:00-17:00

System Validation (IN4387) November 2, 2012, 14:00-17:00 System Vlidtion (IN4387) Novemer 2, 2012, 14:00-17:00 Importnt Notes. The exmintion omprises 5 question in 4 pges. Give omplete explntion nd do not onfine yourself to giving the finl nswer. Good luk! Exerise

More information

Computing on rings by oblivious robots: a unified approach for different tasks

Computing on rings by oblivious robots: a unified approach for different tasks Computing on rings y olivious roots: unifie pproh for ifferent tsks Ginlorenzo D Angelo, Griele Di Stefno, Alfreo Nvrr, Niols Nisse, Krol Suhn To ite this version: Ginlorenzo D Angelo, Griele Di Stefno,

More information

Linear Inequalities. Work Sheet 1

Linear Inequalities. Work Sheet 1 Work Sheet 1 Liner Inequlities Rent--Hep, cr rentl compny,chrges $ 15 per week plus $ 0.0 per mile to rent one of their crs. Suppose you re limited y how much money you cn spend for the week : You cn spend

More information

Introduction to Graphical Models

Introduction to Graphical Models Introution to Grhil Moels Kenji Fukumizu The Institute of Sttistil Mthemtis Comuttionl Methoology in Sttistil Inferene II Introution n Review 2 Grhil Moels Rough Sketh Grhil moels Grh: G V E V: the set

More information