Applied Mathematics and Computation
|
|
- Maximilian Hines
- 5 years ago
- Views:
Transcription
1 Appled Matheatcs and Coputaton 27 (20) Contents lsts avalable at ScenceDrect Appled Matheatcs and Coputaton journal hoepage The explct soluton of the proft axzaton proble wth box-constraned nputs L. Bayón, J.M. Grau, M.M. Ruz, P.M. Suárez Departent of Matheatcs, Unversty of Ovedo, Gjón, Span artcle nfo abstract Keywords Optzaton theory Proft axzaton Cost nzaton Cobb Douglas Box constrants Non-ear prograng Infal convoluton In ths paper we study the proft-axzaton proble, consderng axu constrants for the general case of -nputs and usng the Cobb Douglas odel for the producton functon. To do so, we prevously study the fr s cost nzaton proble, proposng an equvalent nfal convoluton proble for exponental-type functons. Ths study provdes an analytcal expresson of the producton cost functon, whch s found to be a pece-wse potental. Moreover, we prove that ths soluton belongs to class C. Usng ths cost functon, we obtan the explct expresson of axu proft. Fnally, we llustrate the results obtaned n ths paper wth an exaple. Ó 20 Elsever Inc. All rghts reserved.. Introducton One of the ost portant ssues for frs n the feld of croeconocs [] s the proft-axzaton proble. In ths paper we consder a fr that operates under perfect copetton,.e. ts prces are ndependent of the fr s nput and output decsons. Consder a fr eployng a vector of nputs x 2 R þ to produce an output y 2 R þ, where R þ ; R þ are non-negatve - and -densonal Eucldean spaces, respectvely. Let P(x) be the feasble output set for the gven nput vector x and L(y) the nput requreent set for a gven output y. Now, the technology set [2] s defned as T ¼ ðx; yþ 2R þ þ ; x 2 LðyÞ; y 2 PðxÞ We assue that ths set satsfes the followng well-nown regularty propertes closedness, non-eptness, scarcty, and no free lunch. Only on a few occasons have addtonal constrants been eployed n the lterature; for exaple, an expendture constrant s consdered n [3]. Most classcal studes, however, splfy resource utlzaton wthout consderng constrants on nput usage. In ths paper we establsh, for the frst te, a box-constraned proft-axzaton proble, consderng axu constrants for the nputs. Generally, the proft axzaton proble can be forulated n the followng way the fr chooses nputs and output n order to axze profts p (where profts are revenue nus costs), subject to technology constrants (.e. the relatonshp between nputs and output) st pðp; wþ ¼ax py wxþ; x;y y ¼ f ðxþ; ðx; yþ 2T; 0 6 x 6 M ; ¼ ;...; ; ðþ Correspondng author. Address EPI, Departent of Matheatcs, Unversty of Ovedo, Capus of Vesques, Gjón, Span. E-al address bayon@unov.es (L. Bayón) /$ - see front atter Ó 20 Elsever Inc. All rghts reserved. do0.06/j.ac
2 8706 L. Bayón et al. / Appled Matheatcs and Coputaton 27 (20) where p s the prce of the output, w 2 R are the vector prces of the nputs, M the axu constrants for the nputs, and f(x) s the producton functon, whch s a contnuous, strctly ncreasng and strctly quasconcave functon. In ths paper we consder an -nput Cobb Douglas producton functon [4], [5] y ¼ f ðxþ ¼A Y x a There are two ways of solvng ths proble () we can ether forulate the proble axzng over the nput quanttes, wth plan Lagrange/Kuhn Tucer or substtutng the constrant n the objectve functon; or () we forulate the proble usng a nu cost functon and then axze over the output quantty. In ths paper we use ths short-cut-va-cost nzaton. Note that f a fr s axzng ts profts and decdes to offer a level of producton y, t ust be nzng the cost of producng ths output. Otherwse, a cheaper way of obtanng y producton unts would exst, whch would ean that the fr s not be axzng ts profts. Naely proft ax ples cost n. On the other hand, the proft-axzaton proble has tradtonally been solved by dfferentatng the varable x. Nevertheless, soe authors avod usng total dfferentaton of frst-order condtons, ndcatng that ths gves rse to coplcated equatons whch are dffcult to handle. For exaple, [6] and [7] eploy geoetrc prograng to derve the axal proft for the proft functon. In the present paper we obtan the analytcal and explct forulas usng the classcal ethod of calculaton. The followng are coon probles than can arse () The producton functon ay not be dfferentable, n whch case we cannot tae frst-order condtons. () The frst-order condtons gven above assue an nteror soluton, but we ust also consder boundary solutons. () A proft axzng plan ght not exst. (v) The proft axzng producton plan ght not be unque. In ths paper we prove, under certan assuptons, the exstence and unqueness of the soluton and that t belongs to class C. The paper s organzed as follows. Our box-constraned proft-axzaton proble s solved n two stages we frst deterne how to nze the costs of producng each aount y and then what aount of producton actually axzes profts. In the next secton we present the box-constraned cost-nzaton proble. By changng certan varables, we then transfor t nto a non-ear (exponental) separable prograng proble [8], whch we state as a constraned nfal convoluton proble [9]. In Secton 3, we provde a nuber of basc defntons and develop all the atheatcal results necessary for the soluton of the nfal convoluton proble. Secton 4 presents the optal soluton of the box-constraned cost-nzaton proble. In Secton 5, we obtan the optal soluton of the box-constraned proft-axzaton proble to then dscuss the results of a nuercal exaple n Secton 6. Fnally, Secton 7 suarzes the an conclusons of our research. 2. Cost-nzaton proble In ths secton we frst present the classc fr s cost-nzaton proble. Ths proble can be expressed as follows produce a gven output y, and choose nputs to nze ts cost st cðw; yþ ¼n wx; xp0 f ðxþ ¼y; ð2þ where x 2 R are the nputs and w 2 R are the factor prces. There are a nuber of dfferent ways to atheatcally express how nputs are transfored nto output. In ths paper we consder the general Cobb Douglas producton functon y ¼ f ðxþ ¼A Y x a and we shall usually easure unts so that the total factor productvty A =. The su of a deternes the returns to scale. The forulas for the correspondng cost functon c(w, y) are well nown [0] when the producton functon follows the Cobb Douglas odel Y cðw; yþ ¼aya w a a ; a wth a ¼ X a These forulas, whch can be obtaned sply usng the Lagrange ultplers ethod, present the drawbac that they are not applcable when upper lt constrants are consdered for the dfferent nputs. In ths paper we establsh the analytcal expresson for the cost functon c(w, y) usng the Cobb Douglas odel, consderng axu constrants for the nputs. Our cost-nzaton proble wll be
3 L. Bayón et al. / Appled Matheatcs and Coputaton 27 (20) st cðw; yþ ¼n P w x ; y ¼ Q x a ; 0 6 x 6 M ; ¼ ;...; ð3þ Probles of ths nd, wth box constrants, becoe coplcated n the presence of boundary solutons. There s a vast array of software pacages for nuercally solvng nonear optzaton probles []. These ethods only obtan an approxate soluton for specfc values of the output y, but do not provde the analytcal expresson of the cost functon c(w, y). It s thus not possble to now the argnal cost y)/@y needed to solve the proft axzaton proble. We shall address ths proble n an exact way n ths paper, transforng t nto a non-ear (exponental) separable prograng proble, whch we state as a constraned nfal convoluton proble. Tang nto account the followng changes n the varables y ¼ q; a x ¼ z ; ¼ ;...; ; the cost-nzaton proble (3) s equvalent to the nfal convoluton proble st ~cðw; qþ ¼n P a w e z ; P z ¼ q; < z 6 a M ¼ ; ¼ ;...; The functon ~cðw; Þ s, n fact, the nfal convoluton of the exponental functons ð4þ F ðz Þ ¼ w e a z The case of quadratc F functons s well nown and has been studed by the authors n [2] wthn the fraewor of hydrotheral optzaton. However, the sae nd of study s unnown for exponental functons. In ths paper we develop the necessary atheatcal tools to justfy the proposed ethod for solvng the stated proble. 3. Infal convoluton proble Let us calculate the nfal convoluton of the convex functons F (z ) consderng ther doan to be constraned to ð ; Š. Let us assue throughout the paper, wthout loss of generalty, that 6 F 0 ; 8 ¼... ð5þ F 0 þ Pax þ Let the functon F ð ; Šð ; Š!R gven by Fðz ;...; z Þ ¼ X F ðz Þ Let C q be the set (, ) X C q ¼ ðz ; ; z Þ2 ; ; P ax z ¼ q The nfal convoluton of ff g s ðf F ÞðqÞ ¼ n C q X F ðz Þ Let us now see the defntons of the eleents that are present n our proble. Defnton. Let us call the functon W ð ; P W ðqþ ¼z ; 8 ¼ ;...; ; j¼ Pax j where (z,...,z ) s the unque nu of F on the set C q,.e. X W ðqþ ¼q and X F ðw ðqþþ ¼ ðf F ÞðqÞ Š! ð ; Š the th dstrbuton functon, defned by
4 8708 L. Bayón et al. / Appled Matheatcs and Coputaton 27 (20) The followng lea guarantees that f z reaches ts axu value, all those z for whch the dervatve of F at ts axu value s less than or equal to the dervatve correspondng to F wll lewse have already reached ther axu. Lea. If the functon F reaches at (a,...,a ) the nu on the set C q, and for a certan 2f;...; g; a ¼ 6 F 0 ) a ¼ 8 2f;...; g=f 0 Pax, then Proof. We shall argue by contradcton. Let us assue that for a certan 2f;...; g; a ¼ F 0 j j 6 F 0 Consder the functon gðeþ ¼Fða ;...; a j þ e;...; a e;...; a Þ Fða ;...; a Þ; gðeþ ¼F j ða j þ eþþf ða eþ F j ða j Þ F ða Þ It s clear that f (a,...,a ) 2 C q, then (a,...,a j + e,...,a e,...,a ) 2 C q for 0 6 e < j a j and that a j < j, beng Let us show the exstence of an e such that g(e) < 0, whch contradcts the fact that F has a nu n (a,...,a ) wthn C q. We have that g s contnuous and dervable at zero wth g(0) = 0; therefore t suffces to observe that g 0 (0) < 0. In fact, g 0 ðeþ ¼F 0 j ða j þ eþ F 0 ða eþ ¼F 0 j ða j þ eþ F 0 e ; g 0 ð0þ ¼F 0 j ða jþ F 0 < F 0 j j F The followng lea establshes the order of the ponts at whch the varables reach ther axu value. Lea 2. The paraeters satsfy h ¼ X a ¼ P a ax þ X ¼ a a w þ X a w ; h 6 h h ¼ X Proof h ¼ X ¼ þ X a P a ax ¼þ þ X a w ¼ a þ X a a w þ X a w ¼ X ¼þ ¼ X ¼þ a a a þ þ þ X w þ Pax þ X a a a a w þ þ X a þ w ¼þ ¼þ a w a þ X ¼ h þ 6 X ¼þ a w þ þ Pax þ a þ a þ The followng theore establshes a necessary and suffcent condton to obtan the nteror soluton. Theore. The functon F attans ts nu value on the set C q at the pont ða ;...; a Þ2C o q ff q < X a a þ X a a w ¼ h a w Proof. NecesstyIf (a,...,a ) s an nteror pont where F attans ts nu value, t s a pont of relatve nu of F on the set ( ) ðz ;...; z Þ 2 ð ; Þ... ð ; Þ X z ¼ q
5 L. Bayón et al. / Appled Matheatcs and Coputaton 27 (20) It follows that for soe 2 R; ða ;...; a Þ s a crtcal pont of F ðz ;...; z Þ¼Fðz ;...; z Þ ðz þþz qþ Usng the Lagrange ultplers ethod, we have that 9 h 9 w a e z a ¼ 0 z ¼ a w a h w 2 a 2 e z2 a2 ¼ 0 >= z 2 ¼ a 2 w 2 a 2 >=. )... h w a e z a ¼ 0 z ¼ a >; w a >; z þ z 2 þþz ¼ q z þ z 2 þþz ¼ q Hence and ¼ P q þ a ¼ P a " # e q Y a P w a a X w a a Let us consder W (q) to be a functon of the unnown z W ðqþ ¼z ¼ a " X P q þ a # a w ; a a w W ðqþ ¼ a " X P q þ a # a w ¼ P a a ax ; w a ¼ q Lettng D () X ¼ X P a ax þ X a a w a w P a ax þ X a a w a a w and, bearng n nd (5), we see that h ¼ D 6 D D It s evdent that for every, the soluton W (q) s strctly ncreasng as a functon of q. Thus, q P h ) W ðqþ ¼a P W ðh Þ¼ or, conversely, W ðqþ ¼a < ) q < h (Suffcency). Snce C q s copact, the nu of F clearly exsts. Let us now consder ða ;...; a Þ¼ðW ðqþ;...; W ðqþþ; a crtcal pont of the convex functonal where F ðz ;...; z Þ¼Fðz ;...; z Þ ðz þþz qþ; ¼ " # e q Y a P w a a We have that (a,...,a ) delvers the nu value to F and, hence, t s also the nu of F under the constrant ( ) X ðz ;...; z Þ z ¼ q
6 870 L. Bayón et al. / Appled Matheatcs and Coputaton 27 (20) Moreover, t s evdent that for every, the soluton W (q) s strctly ncreasng as a functon of q. Thus, q < h ) q < D ; o so that ða ;...; a Þ2C q. 8 ¼ ;...; ) W ðqþ ¼a < ; 8 ¼ ;...; ; h Havng proven the above results, we are now n a poston to obtan the dstrbuton functons Theore 2. For every =,...,, the th dstrbuton functon s 8 a P q þ P a w a a a f q < h w >< " # W ðqþ ¼ P a a q þ P a w a a ¼jþ Pj w f h j 6 q < h jþ 6 h ¼jþ > f q P h wth the coeffcents h ¼ X ¼ a P a ax þ X ¼ a a w þ X a w Proof. In vew of Theore, fq < h, then the dstrbuton functons W ðqþ < for all and t reans to derve the expresson for z.ifh 6 q < h 2, then the nu of P F ðz Þ cannot be attaned n the nteror. Accordng to Lea, at least z ¼. Thus, W ðqþ ¼. The sae arguent apples to the reanng proble of denson. W ðqþ ¼ a P ¼2a " # q þ X a ¼2 a w a w If h 2 6 q < h 3, then W ðqþ ¼ and, argung as above, W 2 ðqþ ¼ 2, and for > 2, we have that " # W ðqþ ¼ a P ¼3a q 2 þ X ¼3 a w a w a Fnally, repeatng the arguent once agan, we have that f h j 6 q < h j+, then the th dstrbuton functon s equal to q P h, and f h > q ( =,...,j + ), W ðqþ ¼ a P a ¼jþ " X j q þ X a # a w a ¼jþ w We shall also prove that the nfal convoluton of the functons ff g belongs to class C. Let us see the followng lea frst. f Lea 3. Let ff g 2 C ðrþ be two convex functons satsfyng F 0 ðm Þ 6 F 0 2 ðm 2Þ. Let us consder Then ðf F 2 ÞðnÞ ¼ nff ðxþþf 2 ðyþg; D wth D ¼fðx; yþ 2 ð ; M Š ð ; M 2 Šjx þ y ¼ ng ðf F 2 Þ2C ð ; M þ M 2 Š Proof. Let ^g and ^g 2 be the functons of class C that satsfy the followng equalty n ff ðxþþf 2 ðyþg ¼ F ð^g ðnþþ þ F 2 ð^g 2 ðnþþ xþy¼n wth ^g ðnþþ^g 2 ðnþ ¼n and F 0 ð^g ðnþþ ¼ F 0 2 ð^g 2ðnÞÞ8n 2 R. We now have that the nfal convoluton of the functons F constraned to ther respectve doans (,M ] wll be ðf F 2 ÞðnÞ ¼F ðg ðnþþ þ F 2 ðg 2 ðnþþ
7 L. Bayón et al. / Appled Matheatcs and Coputaton 27 (20) wth g 2 ðnþ ¼ n g ðnþ; g ðnþ ¼ M f ^g ðnþ > M ; ^g ðnþ f ^g ðnþ 6 M Let d be such that ^g ðdþ ¼M (note that F 0 ðm Þ¼F 0 2 ð^g 2ðdÞÞ ¼ F 0 2 ðd M ÞÞ ðf F 2 ÞðnÞ ¼ F ðg ðnþþ þ F 2 ðg 2 ðnþþ f n 6 d; F ðm ÞþF 2 ðn M Þ f d < n 6 M þ M 2 In (,d), the functon (F F 2 ) obvously belongs to class C and also n (d,m + M 2 ], snce (F F 2 )(n)=f (M )+F 2 (n M ). The only conflctng pont s d. Let us study the contnuty of (F F 2 )nd ðf F 2 Þðd Þ ¼ F ð^g ðdþþ þ F 2 ðd ^g ðdþþ ¼ F ðm ÞþF 2 ðd M Þ¼ðF F 2 ÞðdþÞ Let us lewse study the contnuty of the dervatve n d ðf F 2 Þ 0 ðd Þ ¼ F 0 ð^g ðdþþ^g 0 ðdþþf0 2 ðd ^g ðdþþð ^g 0 ðdþþ ¼ F0 ð^g ðdþþ ¼ F 0 ðm Þ; ðf F 2 Þ 0 ðdþþ ¼ F 0 2 ðd M Þ¼F 0 ðm Þ Therefore, (F F 2 ) 2 C. h Theore 3. Let ff g C ðrþ. Let us consder Then X ðf F 2 F ÞðnÞ ¼ n F ðx Þ; D ( wth D ¼ ðx ;...x Þ2 Y X ð ; M Š ) x ¼ n ðf F 2 F Þ2C ; X M # Proof. It suffces to reason by nducton, bearng n nd, due to the assocatvty of, that ðf F 2 F Þ¼ðF F ÞF We ay now also obtan the analytcal expresson of (F F 2 F ). Theore 4. The nfal convoluton of the exponental functons F (z ) s an exponental (plus constant) pecewse functon 8 < ðf F 2 F ÞðqÞ ¼ ~w ~a e q f q < h wth the coeffcents ~l ¼ X w e a ; ~a ¼ X a ¼ ; "! # 2 ~w ¼ exp,~a X Y 4~a Moreover, t belongs to class C. ~l þ ~w e q ~a f h 6 q < h j¼ w j a j a j ~a 3 5 Proof. Fro Theore 3, t s evdent that ðf F 2 F Þ2C ; X #
8 872 L. Bayón et al. / Appled Matheatcs and Coputaton 27 (20) Furtherore, the nfal convoluton expresson for the exponental functons ff g s easly obtaned sply tang nto account the defnton of (F F 2 F ), Theore 2 and the fact that W (q)=z, " =,...,. h 4. Soluton of the cost-nzaton proble Consderng the fact that cðw; yþ ¼~cðw; yþ ¼ðF F 2 F Þð yþ, the followng theore s verfed Theore 5. The condtonal deand functon for the th nput s 8 Q >< exp x ðw; yþ ¼ Q > a a w ~a a y w ¼jþ e a Pj a w a w ~a f y < e h ; =~a jþ a ~a jþ y ~a jþ f e h j 6 y < e h jþ 6 e h ; f y P e h and the cost functon s a pecewse potental (plus constant) 8 < cðw; yþ ¼ ~w ~a y f y < e h ; ~a ~l þ ~w y f e h 6 y < e h ; where ~l ; ~a and ~w are the coeffcents defned n Theore 4. Proof. Tang nto consderaton the changes n the varable y ¼ q; z ¼ a x ¼ W ðqþ and Theore 2, we obtan the expresson of the condtonal deand functon for the th nput, x (w,y). Slarly, as cðw; y ¼ ~cðw; yþ, fro Theore 3 we obtan the cost functon expresson c(w,y). h 5. Soluton of the proft-axzaton proble Havng calculated the cost functon C(y) ¼ c(w,y) and havng establshed ts character, C, the proft-axzaton proble pðp; wþ ¼axðpy cðw; yþþ ¼ axðpy CðyÞÞ y y translates nto the deternaton of the optu level of output y for whch the argnal cost concdes wth the prce p. Naturally, ths consderaton s only vald for output levels for whch the argnal cost s ncreasng (C(y) convex). p ¼ C 0 ðy Þ^ convexty of C ) pðp; wþ ¼py Cðy Þ Bearng n nd that the cost functon s pecewse potental, the correct calculaton of the output level requres pror nvestgaton of the nterval ½e h ; e h Š for whch C 0 ðe h Þ 6 p 6 C 0 ðe h Þ Ths queston s trval, as we already have the analytcal expresson of C(y). 6. Exaple We shall now present a proft axzaton proble wth a Cobb Douglas type producton functon whch, wthout consderng techncal constrants for the nputs, would be totally unreal, as t would present ncreasng returns to scale at all levels of producton (and hence a concave cost functon). By consderng nputs to be lted, the proble becoes totally real and the resultng cost functon presents a regon of concavty (ncreasng returns to scale) and another of convexty (decreasng returns to scale) where the soluton to the proble s to be found the level of producton at whch the argnal cost and output prce concde.
9 L. Bayón et al. / Appled Matheatcs and Coputaton 27 (20) We consder the followng exaple st pðp; wþ ¼axðpy wxþ; y ¼ Y x a ; x;y 0 6 x 6 M ; ¼ ;...; wth = 20 nputs, output prce p = 20, and wth the data presented n Table. Table Exaple data a M w a M w c(w,y) Fg.. The cost functon. y Table 2 The pecewse cost functon. c(w,y) y 2 [a,b) y [0,0.30] y [0.30,0.934) y [0.934, ] y [0.3066,0.572] y [0.572, ] y [0.8529, ] y [0.9383,.9077] y [.9077, ] y [2.7568,2.7648] y [2.7648,3.23] y [3.23,3.2972] y [3.2972,3.8202] y [3.8202, ] y.0989 [3.8726,4.924] y.4925 [4.924,5.5063] y.859 [5.5063, ] y [5.7495,6.098] y [6.098, 6.44] y 6.25 [6.44,6.687] y 20. [6.687,6.89]
10 874 L. Bayón et al. / Appled Matheatcs and Coputaton 27 (20) c( w, y)/ y p=20 Increasng y y Fg. 2. The argnal cost functon. Table 3 Soluton for y = x x Fg. shows the graph of the cost functon c(w,y), together wth the resultng graph n the case of not havng consdered constrants for the nputs; note that both concde n the nterval ½0; e h ¼ 030Š. Fro ths pont onward, not consderng constrants leads to a very dfferent cost functon to the correct one. In addton, the area n whch the producton functon presents decreasng returns to scale, and hence a convex cost functon, s hghlghted n grey. The values fe h g 20 ¼ ¼f030; 0934; 03066; 0572; 08529; 09383; 9077; 27568; 27648; 323; 32972; 38202; 38726; 4924; 55063; 57495; 6098; 644; 6687; 689g consttute the dfferent levels of output at whch the paraeters of the cost functon expresson change. These correspond to the levels at whch the dfferent nputs acheve ther axu value, whch, accordng to the theoretcal developent (5), they do so n ths exaple n the followng order f; 0; 7; 2; 8; 8; 5; 2; 9; 9; 3; 6; 3; 7; 5; ; 20; 6; 4; 4g. The analytcal expresson of the pecewse cost functon s presented n Table 2, beng obtaned as shown n Theore 5 Fg. 2 shows the graph of the argnal cost functon, whch, as has already been establshed, s contnuous (.e. the cost functon belongs to C ). It can be seen that there are two ponts for whch the argnal cost s p = 20. Naturally, however, the area represented n whte does not provde the axu value, as t s located n an area of decreasng argnal cost the only axu s obtaned for the output value y = Fnally, n Table 3 we present the condtonal deand functon for the th nput. 7. Conclusons In ths paper we have establshed the analytcal soluton for the classc fr s proft axzaton proble n the general case wth nputs. We have used the Cobb Douglas odel for the producton functon and have consdered, for the frst te, axu constrants for the nputs. Our study has a nuber of advantages over other ethods the exact boundary soluton s obtaned and the ethod s not affected by the sze of the proble. Acnowledgeent Ths wor was supported by the Spansh Governent (MICINN, project MTM ). References [] H.R. Varan, Interedate Mcroeconocs A odern approach, Seventh ed., W.W. Norton & Copany, New Yor, [2] R. Fare, D. Pront, Mult-Output Producton and Dualty Theory and Applcatons, Kluwer Acadec Publshers, Netherlands, 995.
11 L. Bayón et al. / Appled Matheatcs and Coputaton 27 (20) [3] R.E. Just, D.L. Hueth, A. Schtz, The Welfare Econocs of Publc Polcy a Practcal Approach to Project and Polcy Evaluaton, Edward Elgar Pub.,, USA, [4] G.A. Jehle, P.J. Reny, Advanced Mcroeconoc Theory, Second ed., Addson-Wesley, Boston, 200. [5] D.G. Luenberger, Mcroeconoc Theory, McGraw-Hll, 995. [6] S.T. Lu, A geoetrc prograng approach to proft axzaton, Appled Matheatcs and Coputaton 82 (2006) [7] S.T. Lu, Proft axzaton wth quantty dscount an applcaton of geoetrc progra, Appled Matheatcs and Coputaton 90 (2007) [8] I. Grva, S.G. Nash, A. Sofer, Lnear and Nonear Optzaton, SIAM, Phladelpha, [9] J.B. Hrart-Urruty, C. Learéchal, Convex Analyss and Mnzaton Algorths I, Sprnger-Verlag, Ber, 996. [0] D.S. Haeresh, Labor Deand, Prnceton Unversty Press, 996. [] Nonear Prograng Software, <http//neos.cs.anl.gov/>. [2] L. Bayón, J.M. Grau, P.M. Suárez, A new forulaton of the equvalent theral n optzaton of hydrotheral systes, Matheatcal Probles n Engneerng 8 (3) (2002) 8 96.
12
Applied Mathematics Letters
Appled Matheatcs Letters 2 (2) 46 5 Contents lsts avalable at ScenceDrect Appled Matheatcs Letters journal hoepage: wwwelseverco/locate/al Calculaton of coeffcents of a cardnal B-splne Gradr V Mlovanovć
More informationPreference and Demand Examples
Dvson of the Huantes and Socal Scences Preference and Deand Exaples KC Border October, 2002 Revsed Noveber 206 These notes show how to use the Lagrange Karush Kuhn Tucker ultpler theores to solve the proble
More informationWhat is LP? LP is an optimization technique that allocates limited resources among competing activities in the best possible manner.
(C) 998 Gerald B Sheblé, all rghts reserved Lnear Prograng Introducton Contents I. What s LP? II. LP Theor III. The Splex Method IV. Refneents to the Splex Method What s LP? LP s an optzaton technque that
More informationXiangwen Li. March 8th and March 13th, 2001
CS49I Approxaton Algorths The Vertex-Cover Proble Lecture Notes Xangwen L March 8th and March 3th, 00 Absolute Approxaton Gven an optzaton proble P, an algorth A s an approxaton algorth for P f, for an
More informationSystem in Weibull Distribution
Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co
More informationExcess Error, Approximation Error, and Estimation Error
E0 370 Statstcal Learnng Theory Lecture 10 Sep 15, 011 Excess Error, Approxaton Error, and Estaton Error Lecturer: Shvan Agarwal Scrbe: Shvan Agarwal 1 Introducton So far, we have consdered the fnte saple
More informationCHAPTER 6 CONSTRAINED OPTIMIZATION 1: K-T CONDITIONS
Chapter 6: Constraned Optzaton CHAPER 6 CONSRAINED OPIMIZAION : K- CONDIIONS Introducton We now begn our dscusson of gradent-based constraned optzaton. Recall that n Chapter 3 we looked at gradent-based
More informationCHAPTER 7 CONSTRAINED OPTIMIZATION 1: THE KARUSH-KUHN-TUCKER CONDITIONS
CHAPER 7 CONSRAINED OPIMIZAION : HE KARUSH-KUHN-UCKER CONDIIONS 7. Introducton We now begn our dscusson of gradent-based constraned optzaton. Recall that n Chapter 3 we looked at gradent-based unconstraned
More informationQuantum Particle Motion in Physical Space
Adv. Studes Theor. Phys., Vol. 8, 014, no. 1, 7-34 HIKARI Ltd, www.-hkar.co http://dx.do.org/10.1988/astp.014.311136 Quantu Partcle Moton n Physcal Space A. Yu. Saarn Dept. of Physcs, Saara State Techncal
More informationLeast Squares Fitting of Data
Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2014. All Rghts Reserved. Created: July 15, 1999 Last Modfed: February 9, 2008 Contents 1 Lnear Fttng
More informationFinite Fields and Their Applications
Fnte Felds and Ther Applcatons 5 009 796 807 Contents lsts avalable at ScenceDrect Fnte Felds and Ther Applcatons www.elsever.co/locate/ffa Typcal prtve polynoals over nteger resdue rngs Tan Tan a, Wen-Feng
More informationOn Pfaff s solution of the Pfaff problem
Zur Pfaff scen Lösung des Pfaff scen Probles Mat. Ann. 7 (880) 53-530. On Pfaff s soluton of te Pfaff proble By A. MAYER n Lepzg Translated by D. H. Delpenc Te way tat Pfaff adopted for te ntegraton of
More informationLeast Squares Fitting of Data
Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2015. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng
More informationAN ANALYSIS OF A FRACTAL KINETICS CURVE OF SAVAGEAU
AN ANALYI OF A FRACTAL KINETIC CURE OF AAGEAU by John Maloney and Jack Hedel Departent of Matheatcs Unversty of Nebraska at Oaha Oaha, Nebraska 688 Eal addresses: aloney@unoaha.edu, jhedel@unoaha.edu Runnng
More informationSlobodan Lakić. Communicated by R. Van Keer
Serdca Math. J. 21 (1995), 335-344 AN ITERATIVE METHOD FOR THE MATRIX PRINCIPAL n-th ROOT Slobodan Lakć Councated by R. Van Keer In ths paper we gve an teratve ethod to copute the prncpal n-th root and
More informationThe Parity of the Number of Irreducible Factors for Some Pentanomials
The Party of the Nuber of Irreducble Factors for Soe Pentanoals Wolfra Koepf 1, Ryul K 1 Departent of Matheatcs Unversty of Kassel, Kassel, F. R. Gerany Faculty of Matheatcs and Mechancs K Il Sung Unversty,
More informationBAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS. Dariusz Biskup
BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS Darusz Bskup 1. Introducton The paper presents a nonparaetrc procedure for estaton of an unknown functon f n the regresson odel y = f x + ε = N. (1) (
More informationIntegral Transforms and Dual Integral Equations to Solve Heat Equation with Mixed Conditions
Int J Open Probles Copt Math, Vol 7, No 4, Deceber 214 ISSN 1998-6262; Copyrght ICSS Publcaton, 214 www-csrsorg Integral Transfors and Dual Integral Equatons to Solve Heat Equaton wth Mxed Condtons Naser
More informationDenote the function derivatives f(x) in given points. x a b. Using relationships (1.2), polynomials (1.1) are written in the form
SET OF METHODS FO SOUTION THE AUHY POBEM FO STIFF SYSTEMS OF ODINAY DIFFEENTIA EUATIONS AF atypov and YuV Nulchev Insttute of Theoretcal and Appled Mechancs SB AS 639 Novosbrs ussa Introducton A constructon
More informationWelfare Properties of General Equilibrium. What can be said about optimality properties of resource allocation implied by general equilibrium?
APPLIED WELFARE ECONOMICS AND POLICY ANALYSIS Welfare Propertes of General Equlbrum What can be sad about optmalty propertes of resource allocaton mpled by general equlbrum? Any crteron used to compare
More informationON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES
Journal of Algebra, Nuber Theory: Advances and Applcatons Volue 3, Nuber, 05, Pages 3-8 ON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES Feldstrasse 45 CH-8004, Zürch Swtzerland e-al: whurlann@bluewn.ch
More informationFermi-Dirac statistics
UCC/Physcs/MK/EM/October 8, 205 Fer-Drac statstcs Fer-Drac dstrbuton Matter partcles that are eleentary ostly have a type of angular oentu called spn. hese partcles are known to have a agnetc oent whch
More information1 Definition of Rademacher Complexity
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #9 Scrbe: Josh Chen March 5, 2013 We ve spent the past few classes provng bounds on the generalzaton error of PAClearnng algorths for the
More informationComputational and Statistical Learning theory Assignment 4
Coputatonal and Statstcal Learnng theory Assgnent 4 Due: March 2nd Eal solutons to : karthk at ttc dot edu Notatons/Defntons Recall the defnton of saple based Radeacher coplexty : [ ] R S F) := E ɛ {±}
More informationXII.3 The EM (Expectation-Maximization) Algorithm
XII.3 The EM (Expectaton-Maxzaton) Algorth Toshnor Munaata 3/7/06 The EM algorth s a technque to deal wth varous types of ncoplete data or hdden varables. It can be appled to a wde range of learnng probles
More informationChapter 2 A Class of Robust Solution for Linear Bilevel Programming
Chapter 2 A Class of Robust Soluton for Lnear Blevel Programmng Bo Lu, Bo L and Yan L Abstract Under the way of the centralzed decson-makng, the lnear b-level programmng (BLP) whose coeffcents are supposed
More informationAn Optimal Bound for Sum of Square Roots of Special Type of Integers
The Sxth Internatonal Syposu on Operatons Research and Its Applcatons ISORA 06 Xnang, Chna, August 8 12, 2006 Copyrght 2006 ORSC & APORC pp. 206 211 An Optal Bound for Su of Square Roots of Specal Type
More informationOur focus will be on linear systems. A system is linear if it obeys the principle of superposition and homogenity, i.e.
SSTEM MODELLIN In order to solve a control syste proble, the descrptons of the syste and ts coponents ust be put nto a for sutable for analyss and evaluaton. The followng ethods can be used to odel physcal
More informationSolving Fuzzy Linear Programming Problem With Fuzzy Relational Equation Constraint
Intern. J. Fuzz Maeatcal Archve Vol., 0, -0 ISSN: 0 (P, 0 0 (onlne Publshed on 0 Septeber 0 www.researchasc.org Internatonal Journal of Solvng Fuzz Lnear Prograng Proble W Fuzz Relatonal Equaton Constrant
More informationModule 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:
More informationCOS 511: Theoretical Machine Learning
COS 5: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #0 Scrbe: José Sões Ferrera March 06, 203 In the last lecture the concept of Radeacher coplexty was ntroduced, wth the goal of showng that
More informationPerfect Competition and the Nash Bargaining Solution
Perfect Competton and the Nash Barganng Soluton Renhard John Department of Economcs Unversty of Bonn Adenauerallee 24-42 53113 Bonn, Germany emal: rohn@un-bonn.de May 2005 Abstract For a lnear exchange
More informationStudy of the possibility of eliminating the Gibbs paradox within the framework of classical thermodynamics *
tudy of the possblty of elnatng the Gbbs paradox wthn the fraework of classcal therodynacs * V. Ihnatovych Departent of Phlosophy, Natonal echncal Unversty of Ukrane Kyv Polytechnc Insttute, Kyv, Ukrane
More informationPROBABILITY AND STATISTICS Vol. III - Analysis of Variance and Analysis of Covariance - V. Nollau ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE
ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE V. Nollau Insttute of Matheatcal Stochastcs, Techncal Unversty of Dresden, Gerany Keywords: Analyss of varance, least squares ethod, odels wth fxed effects,
More informationDescription of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t
Indeternate Analyss Force Method The force (flexblty) ethod expresses the relatonshps between dsplaceents and forces that exst n a structure. Prary objectve of the force ethod s to deterne the chosen set
More informationOne-sided finite-difference approximations suitable for use with Richardson extrapolation
Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,
More informationINDEX NUMBER THEORY AND MEASUREMENT ECONOMICS. By W.E. Diewert. February CHAPTER 9: Two Stage Aggregation and Homogeneous Weak Separability
IDEX UMBER THEORY AD MEASUREMET ECOOMICS By W.E. Dewert. February 05. CHAPTER 9: Two Stage Aggregaton and Hoogeneous Weak Separablty. Introducton Most statstcal agences use the Laspeyres forula to aggregate
More informationMinimization of l 2 -Norm of the KSOR Operator
ournal of Matheatcs and Statstcs 8 (): 6-70, 0 ISSN 59-36 0 Scence Publcatons do:0.38/jssp.0.6.70 Publshed Onlne 8 () 0 (http://www.thescpub.co/jss.toc) Mnzaton of l -Nor of the KSOR Operator Youssef,
More informationUsing T.O.M to Estimate Parameter of distributions that have not Single Exponential Family
IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran
More information4 Column generation (CG) 4.1 Basics of column generation. 4.2 Applying CG to the Cutting-Stock Problem. Basic Idea of column generation
4 Colun generaton (CG) here are a lot of probles n nteger prograng where even the proble defnton cannot be effcently bounded Specfcally, the nuber of coluns becoes very large herefore, these probles are
More informationTwo Conjectures About Recency Rank Encoding
Internatonal Journal of Matheatcs and Coputer Scence, 0(205, no. 2, 75 84 M CS Two Conjectures About Recency Rank Encodng Chrs Buhse, Peter Johnson, Wlla Lnz 2, Matthew Spson 3 Departent of Matheatcs and
More informationElastic Collisions. Definition: two point masses on which no external forces act collide without losing any energy.
Elastc Collsons Defnton: to pont asses on hch no external forces act collde thout losng any energy v Prerequstes: θ θ collsons n one denson conservaton of oentu and energy occurs frequently n everyday
More informationFoundations of Arithmetic
Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an
More informationPolynomial Barrier Method for Solving Linear Programming Problems
Internatonal Journal o Engneerng & echnology IJE-IJENS Vol: No: 45 Polynoal Barrer Method or Solvng Lnear Prograng Probles Parwad Moengn, Meber, IAENG Abstract In ths wor, we study a class o polynoal ordereven
More informationAffine transformations and convexity
Affne transformatons and convexty The purpose of ths document s to prove some basc propertes of affne transformatons nvolvng convex sets. Here are a few onlne references for background nformaton: http://math.ucr.edu/
More informationThe Minimum Universal Cost Flow in an Infeasible Flow Network
Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran
More informationColumn Generation. Teo Chung-Piaw (NUS) 25 th February 2003, Singapore
Colun Generaton Teo Chung-Paw (NUS) 25 th February 2003, Sngapore 1 Lecture 1.1 Outlne Cuttng Stoc Proble Slde 1 Classcal Integer Prograng Forulaton Set Coverng Forulaton Colun Generaton Approach Connecton
More information3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X
Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number
More informationEconomics 101. Lecture 4 - Equilibrium and Efficiency
Economcs 0 Lecture 4 - Equlbrum and Effcency Intro As dscussed n the prevous lecture, we wll now move from an envronment where we looed at consumers mang decsons n solaton to analyzng economes full of
More informationHow Strong Are Weak Patents? Joseph Farrell and Carl Shapiro. Supplementary Material Licensing Probabilistic Patents to Cournot Oligopolists *
How Strong Are Weak Patents? Joseph Farrell and Carl Shapro Supplementary Materal Lcensng Probablstc Patents to Cournot Olgopolsts * September 007 We study here the specal case n whch downstream competton
More informationOn the number of regions in an m-dimensional space cut by n hyperplanes
6 On the nuber of regons n an -densonal space cut by n hyperplanes Chungwu Ho and Seth Zeran Abstract In ths note we provde a unfor approach for the nuber of bounded regons cut by n hyperplanes n general
More informationNumerical Heat and Mass Transfer
Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and
More informationSolutions HW #2. minimize. Ax = b. Give the dual problem, and make the implicit equality constraints explicit. Solution.
Solutons HW #2 Dual of general LP. Fnd the dual functon of the LP mnmze subject to c T x Gx h Ax = b. Gve the dual problem, and make the mplct equalty constrants explct. Soluton. 1. The Lagrangan s L(x,
More informationConvexity preserving interpolation by splines of arbitrary degree
Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete
More informationarxiv: v2 [math.co] 3 Sep 2017
On the Approxate Asyptotc Statstcal Independence of the Peranents of 0- Matrces arxv:705.0868v2 ath.co 3 Sep 207 Paul Federbush Departent of Matheatcs Unversty of Mchgan Ann Arbor, MI, 4809-043 Septeber
More informationMaximizing the number of nonnegative subsets
Maxmzng the number of nonnegatve subsets Noga Alon Hao Huang December 1, 213 Abstract Gven a set of n real numbers, f the sum of elements of every subset of sze larger than k s negatve, what s the maxmum
More informationOptimal Marketing Strategies for a Customer Data Intermediary. Technical Appendix
Optal Marketng Strateges for a Custoer Data Interedary Techncal Appendx oseph Pancras Unversty of Connectcut School of Busness Marketng Departent 00 Hllsde Road, Unt 04 Storrs, CT 0669-04 oseph.pancras@busness.uconn.edu
More informationVolume 18 Figure 1. Notation 1. Notation 2. Observation 1. Remark 1. Remark 2. Remark 3. Remark 4. Remark 5. Remark 6. Theorem A [2]. Theorem B [2].
Bulletn of Mathematcal Scences and Applcatons Submtted: 016-04-07 ISSN: 78-9634, Vol. 18, pp 1-10 Revsed: 016-09-08 do:10.1805/www.scpress.com/bmsa.18.1 Accepted: 016-10-13 017 ScPress Ltd., Swtzerland
More information1. Statement of the problem
Volue 14, 010 15 ON THE ITERATIVE SOUTION OF A SYSTEM OF DISCRETE TIMOSHENKO EQUATIONS Peradze J. and Tsklaur Z. I. Javakhshvl Tbls State Uversty,, Uversty St., Tbls 0186, Georga Georgan Techcal Uversty,
More informationLECTURE :FACTOR ANALYSIS
LCUR :FACOR ANALYSIS Rta Osadchy Based on Lecture Notes by A. Ng Motvaton Dstrbuton coes fro MoG Have suffcent aount of data: >>n denson Use M to ft Mture of Gaussans nu. of tranng ponts If
More informationAssortment Optimization under MNL
Assortment Optmzaton under MNL Haotan Song Aprl 30, 2017 1 Introducton The assortment optmzaton problem ams to fnd the revenue-maxmzng assortment of products to offer when the prces of products are fxed.
More informationModified parallel multisplitting iterative methods for non-hermitian positive definite systems
Adv Coput ath DOI 0.007/s0444-0-9262-8 odfed parallel ultsplttng teratve ethods for non-hertan postve defnte systes Chuan-Long Wang Guo-Yan eng Xue-Rong Yong Receved: Septeber 20 / Accepted: 4 Noveber
More informationMMA and GCMMA two methods for nonlinear optimization
MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons
More informationDifference Equations
Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1
More informationInner Product. Euclidean Space. Orthonormal Basis. Orthogonal
Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,
More informationAPPENDIX A Some Linear Algebra
APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,
More informationChapter 12 Lyes KADEM [Thermodynamics II] 2007
Chapter 2 Lyes KDEM [Therodynacs II] 2007 Gas Mxtures In ths chapter we wll develop ethods for deternng therodynac propertes of a xture n order to apply the frst law to systes nvolvng xtures. Ths wll be
More informationIntroducing Entropy Distributions
Graubner, Schdt & Proske: Proceedngs of the 6 th Internatonal Probablstc Workshop, Darstadt 8 Introducng Entropy Dstrbutons Noel van Erp & Peter van Gelder Structural Hydraulc Engneerng and Probablstc
More informationLinear Approximation with Regularization and Moving Least Squares
Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...
More informationSome modelling aspects for the Matlab implementation of MMA
Some modellng aspects for the Matlab mplementaton of MMA Krster Svanberg krlle@math.kth.se Optmzaton and Systems Theory Department of Mathematcs KTH, SE 10044 Stockholm September 2004 1. Consdered optmzaton
More informationAsymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation
Nonl. Analyss and Dfferental Equatons, ol., 4, no., 5 - HIKARI Ltd, www.m-har.com http://dx.do.org/.988/nade.4.456 Asymptotcs of the Soluton of a Boundary alue Problem for One-Characterstc Dfferental Equaton
More informationprinceton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg
prnceton unv. F 17 cos 521: Advanced Algorthm Desgn Lecture 7: LP Dualty Lecturer: Matt Wenberg Scrbe: LP Dualty s an extremely useful tool for analyzng structural propertes of lnear programs. Whle there
More informationDigital Signal Processing
Dgtal Sgnal Processng Dscrete-tme System Analyss Manar Mohasen Offce: F8 Emal: manar.subh@ut.ac.r School of IT Engneerng Revew of Precedent Class Contnuous Sgnal The value of the sgnal s avalable over
More informationPhysics 5153 Classical Mechanics. Principle of Virtual Work-1
P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal
More informationA Radon-Nikodym Theorem for Completely Positive Maps
A Radon-Nody Theore for Copletely Postve Maps V P Belavn School of Matheatcal Scences, Unversty of Nottngha, Nottngha NG7 RD E-al: vpb@aths.nott.ac.u and P Staszews Insttute of Physcs, Ncholas Coperncus
More informationEcon107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)
I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes
More informationDesigning Fuzzy Time Series Model Using Generalized Wang s Method and Its application to Forecasting Interest Rate of Bank Indonesia Certificate
The Frst Internatonal Senar on Scence and Technology, Islac Unversty of Indonesa, 4-5 January 009. Desgnng Fuzzy Te Seres odel Usng Generalzed Wang s ethod and Its applcaton to Forecastng Interest Rate
More informationCHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE
CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng
More informationCS294 Topics in Algorithmic Game Theory October 11, Lecture 7
CS294 Topcs n Algorthmc Game Theory October 11, 2011 Lecture 7 Lecturer: Chrstos Papadmtrou Scrbe: Wald Krchene, Vjay Kamble 1 Exchange economy We consder an exchange market wth m agents and n goods. Agent
More informationMath1110 (Spring 2009) Prelim 3 - Solutions
Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.
More informationSeveral generation methods of multinomial distributed random number Tian Lei 1, a,linxihe 1,b,Zhigang Zhang 1,c
Internatonal Conference on Appled Scence and Engneerng Innovaton (ASEI 205) Several generaton ethods of ultnoal dstrbuted rando nuber Tan Le, a,lnhe,b,zhgang Zhang,c School of Matheatcs and Physcs, USTB,
More informationON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION
Advanced Mathematcal Models & Applcatons Vol.3, No.3, 2018, pp.215-222 ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EUATION
More informationFinite Vector Space Representations Ross Bannister Data Assimilation Research Centre, Reading, UK Last updated: 2nd August 2003
Fnte Vector Space epresentatons oss Bannster Data Asslaton esearch Centre, eadng, UK ast updated: 2nd August 2003 Contents What s a lnear vector space?......... 1 About ths docuent............ 2 1. Orthogonal
More informationPerron Vectors of an Irreducible Nonnegative Interval Matrix
Perron Vectors of an Irreducble Nonnegatve Interval Matrx Jr Rohn August 4 2005 Abstract As s well known an rreducble nonnegatve matrx possesses a unquely determned Perron vector. As the man result of
More informationThe lower and upper bounds on Perron root of nonnegative irreducible matrices
Journal of Computatonal Appled Mathematcs 217 (2008) 259 267 wwwelsevercom/locate/cam The lower upper bounds on Perron root of nonnegatve rreducble matrces Guang-Xn Huang a,, Feng Yn b,keguo a a College
More informationChapter One Mixture of Ideal Gases
herodynacs II AA Chapter One Mxture of Ideal Gases. Coposton of a Gas Mxture: Mass and Mole Fractons o deterne the propertes of a xture, we need to now the coposton of the xture as well as the propertes
More informationA NOTE ON CES FUNCTIONS Drago Bergholt, BI Norwegian Business School 2011
A NOTE ON CES FUNCTIONS Drago Bergholt, BI Norwegan Busness School 2011 Functons featurng constant elastcty of substtuton CES are wdely used n appled economcs and fnance. In ths note, I do two thngs. Frst,
More informationOn the Multicriteria Integer Network Flow Problem
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of
More informationCentroid Uncertainty Bounds for Interval Type-2 Fuzzy Sets: Forward and Inverse Problems
Centrod Uncertanty Bounds for Interval Type-2 Fuzzy Sets: Forward and Inverse Probles Jerry M. Mendel and Hongwe Wu Sgnal and Iage Processng Insttute Departent of Electrcal Engneerng Unversty of Southern
More informationChapter 1. Theory of Gravitation
Chapter 1 Theory of Gravtaton In ths chapter a theory of gravtaton n flat space-te s studed whch was consdered n several artcles by the author. Let us assue a flat space-te etrc. Denote by x the co-ordnates
More informationMultiplicative Functions and Möbius Inversion Formula
Multplcatve Functons and Möbus Inverson Forula Zvezdelna Stanova Bereley Math Crcle Drector Mlls College and UC Bereley 1. Multplcatve Functons. Overvew Defnton 1. A functon f : N C s sad to be arthetc.
More informationManaging Capacity Through Reward Programs. on-line companion page. Byung-Do Kim Seoul National University College of Business Administration
Managng Caacty Through eward Programs on-lne comanon age Byung-Do Km Seoul Natonal Unversty College of Busness Admnstraton Mengze Sh Unversty of Toronto otman School of Management Toronto ON M5S E6 Canada
More informationPHYS 705: Classical Mechanics. Calculus of Variations II
1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary
More information1 Review From Last Time
COS 5: Foundatons of Machne Learnng Rob Schapre Lecture #8 Scrbe: Monrul I Sharf Aprl 0, 2003 Revew Fro Last Te Last te, we were talkng about how to odel dstrbutons, and we had ths setup: Gven - exaples
More informatione companion ONLY AVAILABLE IN ELECTRONIC FORM
e copanon ONLY AVAILABLE IN ELECTRONIC FORM Electronc Copanon Decson Analyss wth Geographcally Varyng Outcoes: Preference Models Illustratve Applcatons by Jay Son, Crag W. Krkwood, L. Robn Keller, Operatons
More informationy new = M x old Feature Selection: Linear Transformations Constraint Optimization (insertion)
Feature Selecton: Lnear ransforatons new = M x old Constrant Optzaton (nserton) 3 Proble: Gven an objectve functon f(x) to be optzed and let constrants be gven b h k (x)=c k, ovng constants to the left,
More informationOutline. Prior Information and Subjective Probability. Subjective Probability. The Histogram Approach. Subjective Determination of the Prior Density
Outlne Pror Inforaton and Subjectve Probablty u89603 1 Subjectve Probablty Subjectve Deternaton of the Pror Densty Nonnforatve Prors Maxu Entropy Prors Usng the Margnal Dstrbuton to Deterne the Pror Herarchcal
More informationPower law and dimension of the maximum value for belief distribution with the max Deng entropy
Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy Bngy Kang a, a College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Abstract Deng
More informationChapter 3: Oligopoly
Notes on Chapter 3: Olgopoly Mcroeconoc Theory IV 3º - LE-: 008-009 Iñak Agurre Departaento de Fundaentos del Análss Econóco I Unversdad del País Vasco Mcroeconoc Theory IV Olgopoly Introducton.. The Cournot
More informationACTM State Calculus Competition Saturday April 30, 2011
ACTM State Calculus Competton Saturday Aprl 30, 2011 ACTM State Calculus Competton Sprng 2011 Page 1 Instructons: For questons 1 through 25, mark the best answer choce on the answer sheet provde Afterward
More information