Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading

Size: px
Start display at page:

Download "Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading"

Transcription

1 Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng problem n equaon (5) s feasble and does no have he unbound opmal value. I s clear ha he rank of he coeffcen marx for (5) s. In oher words each basc feasble soluon has no more han non-zero elemens. Therefore s opmal o produce wh no more han wo echnologes for any ( qr ) Θ. Proof of Lemma From he dscusson n subsecon 4. and he algorhm ha deermnes he effecve echnology se S we know ha f r μ q r μ ( = L m ) hen s opmal o produce wh echnologes and n S. The producon quanes of echnologes and are q = ( r μq) ( μ μ) and q = ( μ q r) ( μ μ) respecvely and he mnmum producon cos s ha F( qr ) = ( μ q r)( c c ) ( μ μ ) + c q= ω q vr = L m. When fndng echnology + n S Sep 3 n he algorhm ha deermnes S mples Consequenly for = L m v ( c c ) ( μ μ ) > ( c c ) ( μ μ ) = L m c c c c ( c c ) ( c c ) c c v+ = = > μ+ μ+ μ μ+ ( μ μ+ ) ( μ μ+ ) μ μ+ I follows ha ω+ ω+ = ( c+ + μ+ v+ ) ( c+ + μ+ v+ ) = μ+ v+ μ+ v+ >. Snce μ > L > μ m and < c < L < cm we have < v < L < vm and < ω < L < ω m.

2 Proof of Lemma 3 We frs show he convexy of F( qr ). For any r and r le q [ r μ r μ ] and q [ r μ r μ ]. Lemma mples ha where F( q r ) = c q + cq and F( q r ) = c q + c q q + q = q μ q + μq = r q + q = q and μ q + μq = r Therefore for any λ []. λf( q r ) + ( λ) F( q r ) = c ( λq ) + c ( λq ) + c [( λ) q ] + c [( λ) q ] F[ λq + ( λ) q λr + ( λ) r ]. The nequaly holds because λq λ q ( λ) q and ( ) ha mnmze he producon cos under gven ( λq + ( λ) q λr + ( λ) r ). Consequenly F( qr ) s convex. r λ q are he feasble soluons Nex we prove he submodulary of F( qr ). We only need o show ha for all r q q and F( q r ) F( q r ) F( q r ) F( q r ). (A.) We verfy wheher equaon (A.) holds or no n wo cases: () here exss such ha r μ q q r μ r μ q r μ < r μ ; and () here exs and such ha +. q r μ + + Case In hs case r μ q q r μ ( ) ( ) ω ω ω( ). r μ q q r μ ; r μ. q r μ < r μ + q r μ + + F q r F q r = q vr q + vr = q q We dscuss wo subcases: () here exss such ha such ha Snce Subcase In hs subcase r r r μ q q r μ and () here exs and F( q r ) F( q r ) = ω q v r ω q + v r = ω ( q q ). s clear ha ω ω. Consequenly Subcase I s clear ha μ μ or equvalenly. From Lemma hs mples ha F( q r) Fq ( r) = ω ( q q) ω ( q q) = Fq ( r) Fq ( r). r μ q r μ < r μ q r μ r q r < L < r + q r μ μ μ μ. From Subcase we have F( r μ r) Fq ( r) Fr ( μ r) Fq ( r)

3 F( r μ r ) F( r μ r ) F( r μ r ) F( r μ r ) = L F( q r ) F( r μ r ) F( q r ) F( r μ r ). Combnng he above equaons we have Case + + F q r F q r F q r F q r r μ q r μ < r μ q r μ ( ) ( ) ( ) ( ) From Case we know ha F( r μ r ) F( q r ) F( r μ r ) F( q r ) F( r μ r ) F( r μ r ) F( r μ r ) F( r μ r ) = L Combnng he above equaons we have F( q r ) F( r μ r ) F( q r ) F( r μ r ). + + F q r F q r F q r F q r ( ) ( ) ( ) ( ). Equaon (A.) holds n boh Case and Case. Therefore F( qr ) s submodular. In summary F( qr ) s convex and submodular. Proof of Lemma 5 Le W % ( x z) = W( x z) and H% ( y w ) = H ( y w ). Then W% ( x z) = mn { F( q r) + H% ( x q z r)} ( qr ) Θ = mn q+ y= x r+ w= z ( q r y w) Ω{ F( qr ) + H % ( yw )} where Ω= {( qryw ) : r rμ q rμm yw }. I can be verfed ha Ω s a nonempy closed convex sublace. The supermodulary and convexy of H mply ha H % s submodular and convex. From Theorem n Chen e al. (3) we have ha W% ( x z) s submodular and convex n. I follows ha W( x z ) s supermodular and convex. Proof of Proposon The supermodulary and convexy of H ( y w K ) W ( x z K ) and V ( x z K ) can be proved by nducon. I s clear ha V ( ) T + x z s supermodular and convex. Suppose ha V ( ) + x z K s supermodular and convex. Ths mples ha H ( y w K) = G ( y) + E [ V ( y D w K% )] s also supermodular and convex because () γ + + G ( y) s convex and () boh supermodulary and convexy are preserved under expecaon and 3

4 lnear operaon. As shown n Lemma F( qr ) s submodular and convex. Lemma 5 mples ha W ( x z K) = mn{ F( q r) + H ( x+ q z r K ):( q r) Θ} s supermodular and convex so W ( x z K ) s submodular and convex n ( x z ). Snce C s a convex funcon C ( z z) s submodular and convex n ( zz ) (Topks 998). I s well known ha submodulary and convexy are preserved under mnmzaon. Accordngly we have he submodulary and convexy of V ( x z K) = mn{ C ( z z K) + W ( x z K): z } n ( x z ). Ths mples ha V ( x z K ) s supermodular and convex. In summary H ( y w K ) W ( x z K ) and V ( x z K ) are boh supermodular and convex under any gven K for all perods. Proof of Theorem Before provng Theorem we rewre problem (3) based on he formula of F( qr ). For = L m we defne W ( x z K) mn μ μ { ω y+ vw+ H ( y w K)} ω x v z w z( z w) y x ( z w) V ( x z K) mn{mn { K ( z z) + W ( x z K)}mn { k ( z z) + W ( x z K)}}. z z z z Then problem (3) can be rewren as V ( x z K) = mn{ V ( x z K) L V ( x z K)}. m Here V ( x z K ) s he opmal cos for he problem usng us echnologes and. Based on Lemma 5 s easy o verfy he supermodulary and convexy of W ( x z K ) and V ( x z K ). Defne he followng hresholds for he allowance level: L ( x K) argmn { K z W ( x z K)} z + K z + z + z + U ( x ) argmn { k z W ( x z K)} = L m l ( x K) sup{ z :( W ( x z K)) < v} u ( x K) nf{ z :( W ( x z K)) > v} = L m. I s easy o verfy ha () L ( x K) U ( x K ) ( k K ) and () L ( x K) l ( x K) u ( x K) U ( x K ) f k < v < K from he convexy of W ( x z K ). Smlar o he proof of Theorems -4 n Gong and Zhou (3) can be proved from s supermodulary and convexy ha he opmal soluons o V ( x z K ) have he followng properes leng y and z be he opmal soluons o V ( x z K ): 4

5 LEMMA A.. The followng resuls hold: () If v K hen produce exclusvely wh echnology : when z U ( x K ) sell allowances down o z = U ( x K ) and hen produce up o ˆU y = max{ S ( K ) x}; when L ( x K) < z< U ( x K ) do no rade allowances and hen produce up o y = max{ s ( μ x+ z K ) x}; and when z L ( x K ) purchase allowances up o z = L ( x K ) and hen produce up o ˆ L y = max{ S ( K ) x}. () If v k hen produce exclusvely wh echnology : when z U ( x K ) sell allowances down o z = U ( x K ) and hen produce up o max{ ˆU y = S ( K ) x}; when L ( x K) < z< U ( x K ) do no rade allowances and hen produce up o y = max{ s ( μ x+ z K ) x}; and when z L ( x K ) purchase allowances up o z = L ( x K ) and hen produce up o y = max{ S ( K ) x}. 5 ˆ L () If k < v < K hen when z U ( x K ) sell allowances down o z = U ( x K ) and hen produce exclusvely wh echnology up o ˆU y = max{ S ( K ) x}; when u ( x K) z< U ( x K ) do no rade allowances and hen produce exclusvely wh echnology up o y = max{ s ( μ x+ z K ) x}; when l ( x K) < z< u ( x K ) do no rade allowances and hen produce [ μx+ z μs( K) w( K )] ( μ μ) uns wh echnology and [ μ S( K) + w( K ) μ x z] ( μ μ) uns wh echnology up o y = S ( K ) ; when L ( x K) < z l ( x K ) do no rade allowances and hen produce exclusvely wh echnology up o purchase allowances up o o y = max{ S ( K ) x}. ˆ L y = max{ s ( μ x+ z K ) x}; and when z L ( x K ) z = L ( x K ) and hen produce exclusvely wh echnology up The defnons of ˆ U S ( K ) ˆ L S ( K ) s ( μ x+ z K ) S ( K ) and w ( K ) can be found n Secon 5. In Lemma A. L ( x K ) U ( x K ) l ( x K ) and u ( x K ) have he followng specfc formulas: ˆL + L ( ) ( ( ) ) (max{ ˆL L x K = μ S K x + w S ( K) x}) ˆU + U ( ) ( ( ) ) (max{ ˆU U x K = μ S K x + w S ( K ) x}) = L m + l ( x K) = μ ( S ( K) x) + w (max{ S ( K) x}) u ( x K) = μ ( S ( K) x) + w (max{ S ( K ) x}) = L m. + Noe ha x< S ( K ) when l ( x K) < z< u ( x K ). From Lemma A. we know ha he opmal soluons o V ( x z K ) have he followng properes: () If v K hen produce

6 exclusvely wh echnology () If v k hen produce exclusvely wh echnology () If k < v < K hen (a) produce exclusvely wh echnology when z u ( x K ) (b) produce wh echnologes and smulaneously when l ( x K) < z< u ( x K ) and (c) produce exclusvely wh echnology when z l ( x K ). Lemma A. shows he opmal soluons y z and he correspondng echnology selecon for V ( x z K ). Please noe ha producng wh echnology ( = L m ) s feasble o boh V ( x z K ) and V+ ( x z K ). Consequenly he opmal soluons o V ( x z K) = mn{ V ( x z K ) L V ( x z K )} can be characerzed by dscussng he opmal m soluons o V ( x z K) L V ( x z K) as shown n Lemma A.. For example f he opmal m soluon o V ( x z K ) s also feasble o V+ ( x z K ) hen mn{ V ( x z K) V + ( x z K)} = V+ ( x z K ). We nex characerze he srucures of he opmal polces when v K vm k and v k K v + respecvely. Case K v < L < vm From Lemma A.() we know ha when v K he opmal soluon o V ( x z K ) s producng exclusvely wh echnology. Snce producng exclusvely wh echnology ( = 3 L m ) s feasble o boh V ( x z K ) and V ( x z K ) we have V ( x z K ) = mn{ V ( x z K) L V ( x z K)} = V ( x z K). Consequenly he opmal polces have he m followng srucure: when z U ( ) x K sell allowances down o z ( ) = U x K and hen produce exclusvely wh echnology up o ˆ U y = max{ S ( K ) x}; when L ( x K ) < z < U ( ) x K do no rade allowances and hen produce exclusvely wh echnology up o y = max{ s ( μx+ z K ) x}; and when z L ( ) x K purchase allowances up o ( ) z = L x K and hen produce exclusvely wh echnology up o ˆ L y = max{ S ( K ) x}. Case v < L < vm k From Lemma A.() we know ha when v k he opmal soluon o V ( x z K ) s producng exclusvely wh echnology. Snce producng exclusvely wh echnology ( = L m ) s feasble o boh V ( x z K ) and V+ ( x z K ) we have m m V ( x z K) = mn{ V ( x z K) L V ( x z K)} = V ( x z K). Consequenly he opmal polces have he followng srucure: when z U ( x K ) sell allowances down o z = U ( x K ) and hen produce exclusvely wh echnology m up o m m 6

7 y = max{ S ( K ) x}; when L ( x K) < z< U ( x K ) do no rade allowances and hen ˆU m m m produce exclusvely wh echnology m up o y = max{ s ( μ x+ z K ) x}; and when m m z Lm ( x K ) purchase allowances up o z = Lm( x K ) and hen produce exclusvely wh echnology m up o max{ ˆ L y = S ( K ) x}. Case 3 v < L< v k K v < L < v + m Accordng o he analyses n Cases and we have m V( xz K) = mn{ V ( xz K) L V ( xz K)} = mn{ V ( xz K) V ( xz K)}. m + From Lemma A.( ) we know ha he opmal soluons o V ( ) x z K and V + ( x z K ) are he same. Thus he srucure of he opmal polces s: when z U ( ) x K sell allowances down o z = U ( x K ) and hen produce exclusvely wh echnology up o y = max{ S ( K ) x}; when ˆU L ( x K) < z< U ( x K ) do no rade allowances and hen produce exclusvely wh echnology up o y = max{ s ( ) }; μ x+ z K x and when ( ) z L x K purchase allowances up o z ( ) = L x K and hen produce exclusvely wh echnology up o ˆ L y = max{ S ( ) }. K x Proof of Theorem Recall ha v L v k v L v K v L v Smlar o Cases and n he < < < + < < < + < < m. proof of Theorem we have We frs consder v mn{ V ( x z K) L V ( x z K)} = V ( x z K) (A.9) mn{ V ( x z K) L V ( x z K)} = V ( x z K). (A.) + m + mn{ V ( x z K) V ( x z K )} and + mn{ V ( x z K) V ( x z K )}. Snce + k Lemma A.() ndcaes ha he opmal soluon o V ( x z K ) s producng exclusvely wh echnology. I s clear ha hs soluon s also feasble o Consequenly Smlarly from + + V ( ). + x z K mn{ V ( x z K) V ( x z K)} = V ( x z K ). (A.) v < K v + and Lemma A.() we have Concludng equaons (A.9-) we have mn{ V ( x z K) V ( x z K)} V ( x z K ). (A.) + = V ( x z K) = mn{ V ( x z K) L V ( x z K)} = mn{ V ( x z K) L V ( x z K)}. (A.3) m + If = + Lemma A.() ndcaes ha he opmal polces have he same srucure as 7

8 shown n Theorem. We nex consder he case wh = +. Snce k < v + ( v + < K) we have c + + kμ + > c + k μ ( c + + μ + K > c + + μ K + ). From he defnons of ˆ U S ( ) K and ˆ L S ( K ) we have ˆU ˆU S + ( K) S ( K ) and ˆL ( ) ˆL S K S ( K ). Therefore + + Defne U + ( x K) U ( ) x K and L + ( x K) L + ( x K ) for all x. (A.4) y% max{ s + ( μ + x+ z K ) x}. Now we dscuss he relaonshp beween u + ( x K ) and l + ( x K ). From Lemma A. we have W + ( x z K) = c ( y% + x) + H( y% μ + x+ z μ + y% K) when u + ( x K) z< U + ( x K ) W + ( x z K) = c ( y% + x) + H( y% μ + x+ z μ + y% K) when L + ( x K) < z l + ( x K ). I s clear ha u + ( x K ) l ( ) + x K and L ( ) + x K can be rewren as: u ( x K) = nf{ z :( W ( x z K)) > v u ( x K) z< U ( x K)} Snce z l ( x K) = sup{ z :( W ( x z K)) < v L ( x K) < z l ( x K)} z L ( x K) = nf{ z : ( W ( x z K)) > K L ( x K) < z l ( x K)} z < + < he convexes of W + ( x z K ) and W ( x z ) v v K L ( x K) u ( x K) l ( x K ) for all x K mply ha Recall ha Ln ( x K) ln ( x K) un ( x K) Un ( x K ) n= + +. We nex characerze he srucure of he opmal polces when = + n fve cases. (A.5) Case z U ( ) x K Lemma A.() ndcaes ha when z U ( x K ) s opmal o produce exclusvely wh echnology ( = + + ) and sell allowances. From (A.4)) we have mn{ V ( x z K) V ( x z K)} = V ( x z K ) when U ( x ) U ( x K ) (equaon + K z U ( ) x K (A.6) Because producng exclusvely wh echnology + wh sellng allowances s feasble o V ( ) + x z K when z U ( ). x K Equaon (A.6) ndcaes ha when z U ( ) x K s opmal o sell allowances down o level z = U ( x K ) and hen produce exclusvely wh echnology up o U y = max{ S ( ) } K x. Case l + ( x K) < z< U ( ) x K Snce z l ( x K) u ( x K ) (equaon (A.5)) Lemma A.() ndcaes ha he ˆ + + opmal soluon o V + ( x z K ) s producng exclusvely wh echnology +. Ths soluon s clearly feasble o V ( ) + x z K. Hence 8

9 mn{ V + ( x z K) V + ( x z K)} = V + ( x z K ) when l + ( x K) < z< U ( x K ). Ths mples ha: () when u ( ) ( ) + x K z< U x K do no rade allowances and hen produce exclusvely wh echnology up o y = max{ s ( ) }; μ x+ z K x and () when l ( x K) < z< u ( x K) do no rade allowances and hen produce + + ( μ + x+ z μ + S+ ( K) w + ( K )) ( μ μ + ) uns wh echnology and ( μ S ( K) + w ( K ) μ x z) ( μ μ ) uns wh echnology Case 3 u ( x K) z l ( x K ) + + Snce L + ( x K) u + ( x K ) (equaon (A.5)) and l + ( x K) U + ( x K ) Lemma A.() mples ha V + ( x z K ) and V + ( x z K ) have he same opmal soluon when u ( x K) z l ( x K ). Therefore when u ( x K) z l ( x K ) do no rade allowances and hen produce exclusvely wh echnology + up o y = max{ s ( μ x+ z K ) x}. + + Case 4 Snce L ( x K) < z< u ( x K ) + + z u ( x K) l ( x K ) (equaon (A.5)) Lemma A.() suggess ha he + + opmal soluon o V ( ) + x z K s producng exclusvely wh echnology +. Ths soluon s also feasble o V + ( x z K ). Consequenly mn{ V + ( x z K) V + ( x z K)} = V + ( x z K ) when L + ( x K) < z< u + ( x K ). Ths mples ha: () when L + ( x K) z< l + ( x K ) do no rade allowances and hen produce exclusvely wh echnology + up o y = max{ s ( μ x+ z K ) x}; and () when l ( x K) < z< u ( x K) do no rade allowances and hen produce ( μ x+ z μ S ( K) w ( K)) ( μ μ ) uns wh echnology + and ( μ S ( K) + w ( K ) μ x z) ( μ μ ) uns wh echnology Case 5 z L + ( x) Lemma A.() ndcaes ha s opmal o produce exclusvely wh echnology ( = + + ) and purchase allowances when z L ( x K ). From L + ( x K) L + ( x K ) (equaon (A.4)) we have mn{ V ( x z K) V ( x z K)} = V ( x z K ) when z L + ( x K ) (A.7) because producng exclusvely wh echnology + wh purchasng allowances s feasble o V + ( x z K ) when z L + ( x K ). Equaon (A.7) ndcaes ha: when z L + ( x K ) s opmal o purchase allowances up o z = L ( x K ) and hen produce exclusvely wh + 9

10 echnology + up o y = max{ Sˆ ( K ) x}. L + So far we have fully characerzed he opmal polces for he case wh = + whch has he same srucure as shown n Theorem. Smlarly for > + can be proved ha he opmal polcy has he same srucure as shown n Theorem. Deals are omed for brevy.

Epistemic Game Theory: Online Appendix

Epistemic Game Theory: Online Appendix Epsemc Game Theory: Onlne Appendx Edde Dekel Lucano Pomao Marcano Snscalch July 18, 2014 Prelmnares Fx a fne ype srucure T I, S, T, β I and a probably µ S T. Le T µ I, S, T µ, βµ I be a ype srucure ha

More information

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

( ) () we define the interaction representation by the unitary transformation () = ()

( ) () we define the interaction representation by the unitary transformation () = () Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger

More information

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

arxiv: v1 [cs.sy] 2 Sep 2014

arxiv: v1 [cs.sy] 2 Sep 2014 Noname manuscrp No. wll be nsered by he edor Sgnalng for Decenralzed Roung n a Queueng Nework Y Ouyang Demoshens Tenekezs Receved: dae / Acceped: dae arxv:409.0887v [cs.sy] Sep 04 Absrac A dscree-me decenralzed

More information

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon

More information

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or

More information

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function MACROECONOMIC THEORY T J KEHOE ECON 87 SPRING 5 PROBLEM SET # Conder an overlappng generaon economy le ha n queon 5 on problem e n whch conumer lve for perod The uly funcon of he conumer born n perod,

More information

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue. Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons

More information

Comb Filters. Comb Filters

Comb Filters. Comb Filters The smple flers dscussed so far are characered eher by a sngle passband and/or a sngle sopband There are applcaons where flers wh mulple passbands and sopbands are requred Thecomb fler s an example of

More information

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005 Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

Tight results for Next Fit and Worst Fit with resource augmentation

Tight results for Next Fit and Worst Fit with resource augmentation Tgh resuls for Nex F and Wors F wh resource augmenaon Joan Boyar Leah Epsen Asaf Levn Asrac I s well known ha he wo smple algorhms for he classc n packng prolem, NF and WF oh have an approxmaon rao of

More information

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts Onlne Appendx for Sraegc safey socs n supply chans wh evolvng forecass Tor Schoenmeyr Sephen C. Graves Opsolar, Inc. 332 Hunwood Avenue Hayward, CA 94544 A. P. Sloan School of Managemen Massachuses Insue

More information

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal

More information

Robust and Accurate Cancer Classification with Gene Expression Profiling

Robust and Accurate Cancer Classification with Gene Expression Profiling Robus and Accurae Cancer Classfcaon wh Gene Expresson Proflng (Compuaonal ysems Bology, 2005) Auhor: Hafeng L, Keshu Zhang, ao Jang Oulne Background LDA (lnear dscrmnan analyss) and small sample sze problem

More information

Testing a new idea to solve the P = NP problem with mathematical induction

Testing a new idea to solve the P = NP problem with mathematical induction Tesng a new dea o solve he P = NP problem wh mahemacal nducon Bacground P and NP are wo classes (ses) of languages n Compuer Scence An open problem s wheher P = NP Ths paper ess a new dea o compare he

More information

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue. Mah E-b Lecure #0 Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons are

More information

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran

More information

FI 3103 Quantum Physics

FI 3103 Quantum Physics /9/4 FI 33 Quanum Physcs Aleander A. Iskandar Physcs of Magnesm and Phooncs Research Grou Insu Teknolog Bandung Basc Conces n Quanum Physcs Probably and Eecaon Value Hesenberg Uncerany Prncle Wave Funcon

More information

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy

More information

Modeling and Solving of Multi-Product Inventory Lot-Sizing with Supplier Selection under Quantity Discounts

Modeling and Solving of Multi-Product Inventory Lot-Sizing with Supplier Selection under Quantity Discounts nernaonal ournal of Appled Engneerng Research SSN 0973-4562 Volume 13, Number 10 (2018) pp. 8708-8713 Modelng and Solvng of Mul-Produc nvenory Lo-Szng wh Suppler Selecon under Quany Dscouns Naapa anchanaruangrong

More information

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas)

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas) Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on

More information

Comparison of Differences between Power Means 1

Comparison of Differences between Power Means 1 In. Journal of Mah. Analyss, Vol. 7, 203, no., 5-55 Comparson of Dfferences beween Power Means Chang-An Tan, Guanghua Sh and Fe Zuo College of Mahemacs and Informaon Scence Henan Normal Unversy, 453007,

More information

CS286.2 Lecture 14: Quantum de Finetti Theorems II

CS286.2 Lecture 14: Quantum de Finetti Theorems II CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2

More information

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs

More information

( ) [ ] MAP Decision Rule

( ) [ ] MAP Decision Rule Announcemens Bayes Decson Theory wh Normal Dsrbuons HW0 due oday HW o be assgned soon Proec descrpon posed Bomercs CSE 90 Lecure 4 CSE90, Sprng 04 CSE90, Sprng 04 Key Probables 4 ω class label X feaure

More information

Existence and Uniqueness Results for Random Impulsive Integro-Differential Equation

Existence and Uniqueness Results for Random Impulsive Integro-Differential Equation Global Journal of Pure and Appled Mahemacs. ISSN 973-768 Volume 4, Number 6 (8), pp. 89-87 Research Inda Publcaons hp://www.rpublcaon.com Exsence and Unqueness Resuls for Random Impulsve Inegro-Dfferenal

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon If we say wh one bass se, properes vary only because of changes n he coeffcens weghng each bass se funcon x = h< Ix > - hs s how we calculae

More information

ECON 8105 FALL 2017 ANSWERS TO MIDTERM EXAMINATION

ECON 8105 FALL 2017 ANSWERS TO MIDTERM EXAMINATION MACROECONOMIC THEORY T. J. KEHOE ECON 85 FALL 7 ANSWERS TO MIDTERM EXAMINATION. (a) Wh an Arrow-Debreu markes sruure fuures markes for goods are open n perod. Consumers rade fuures onras among hemselves.

More information

Advanced Machine Learning & Perception

Advanced Machine Learning & Perception Advanced Machne Learnng & Percepon Insrucor: Tony Jebara SVM Feaure & Kernel Selecon SVM Eensons Feaure Selecon (Flerng and Wrappng) SVM Feaure Selecon SVM Kernel Selecon SVM Eensons Classfcaon Feaure/Kernel

More information

Volatility Interpolation

Volatility Interpolation Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure

More information

Should Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth

Should Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth Should Exac Index umbers have Sandard Errors? Theory and Applcaon o Asan Growh Rober C. Feensra Marshall B. Rensdorf ovember 003 Proof of Proposon APPEDIX () Frs, we wll derve he convenonal Sao-Vara prce

More information

2/20/2013. EE 101 Midterm 2 Review

2/20/2013. EE 101 Midterm 2 Review //3 EE Mderm eew //3 Volage-mplfer Model The npu ressance s he equalen ressance see when lookng no he npu ermnals of he amplfer. o s he oupu ressance. I causes he oupu olage o decrease as he load ressance

More information

Advanced Macroeconomics II: Exchange economy

Advanced Macroeconomics II: Exchange economy Advanced Macroeconomcs II: Exchange economy Krzyszof Makarsk 1 Smple deermnsc dynamc model. 1.1 Inroducon Inroducon Smple deermnsc dynamc model. Defnons of equlbrum: Arrow-Debreu Sequenal Recursve Equvalence

More information

Modern Dynamic Asset Pricing Models

Modern Dynamic Asset Pricing Models Modern Dynamc Asse Prcng Models Lecure Noes 2. Equlbrum wh Complee Markes 1 Pero Verones The Unversy of Chcago Booh School of Busness CEPR, NBER 1 These eachng noes draw heavly on Duffe (1996, Chapers

More information

On elements with index of the form 2 a 3 b in a parametric family of biquadratic elds

On elements with index of the form 2 a 3 b in a parametric family of biquadratic elds On elemens wh ndex of he form a 3 b n a paramerc famly of bquadrac elds Bora JadrevĆ Absrac In hs paper we gve some resuls abou prmve negral elemens p(c p n he famly of bcyclc bquadrac elds L c = Q ) c;

More information

Teaching Notes #2 Equilibrium with Complete Markets 1

Teaching Notes #2 Equilibrium with Complete Markets 1 Teachng Noes #2 Equlbrum wh Complee Markes 1 Pero Verones Graduae School of Busness Unversy of Chcago Busness 35909 Sprng 2005 c by Pero Verones Ths Verson: November 17, 2005 1 These eachng noes draw heavly

More information

Lecture 2 M/G/1 queues. M/G/1-queue

Lecture 2 M/G/1 queues. M/G/1-queue Lecure M/G/ queues M/G/-queue Posson arrval process Arbrary servce me dsrbuon Sngle server To deermne he sae of he sysem a me, we mus now The number of cusomers n he sysems N() Tme ha he cusomer currenly

More information

Econ107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6)

Econ107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6) Econ7 Appled Economercs Topc 5: Specfcaon: Choosng Independen Varables (Sudenmund, Chaper 6 Specfcaon errors ha we wll deal wh: wrong ndependen varable; wrong funconal form. Ths lecure deals wh wrong ndependen

More information

Lecture 6: Learning for Control (Generalised Linear Regression)

Lecture 6: Learning for Control (Generalised Linear Regression) Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson

More information

Chapter Lagrangian Interpolation

Chapter Lagrangian Interpolation Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and

More information

Cubic Bezier Homotopy Function for Solving Exponential Equations

Cubic Bezier Homotopy Function for Solving Exponential Equations Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon Alernae approach o second order specra: ask abou x magnezaon nsead of energes and ranson probables. If we say wh one bass se, properes vary

More information

Example: MOSFET Amplifier Distortion

Example: MOSFET Amplifier Distortion 4/25/2011 Example MSFET Amplfer Dsoron 1/9 Example: MSFET Amplfer Dsoron Recall hs crcu from a prevous handou: ( ) = I ( ) D D d 15.0 V RD = 5K v ( ) = V v ( ) D o v( ) - K = 2 0.25 ma/v V = 2.0 V 40V.

More information

TSS = SST + SSE An orthogonal partition of the total SS

TSS = SST + SSE An orthogonal partition of the total SS ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally

More information

Variants of Pegasos. December 11, 2009

Variants of Pegasos. December 11, 2009 Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on

More information

First-order piecewise-linear dynamic circuits

First-order piecewise-linear dynamic circuits Frs-order pecewse-lnear dynamc crcus. Fndng he soluon We wll sudy rs-order dynamc crcus composed o a nonlnear resse one-por, ermnaed eher by a lnear capacor or a lnear nducor (see Fg.. Nonlnear resse one-por

More information

Reactive Methods to Solve the Berth AllocationProblem with Stochastic Arrival and Handling Times

Reactive Methods to Solve the Berth AllocationProblem with Stochastic Arrival and Handling Times Reacve Mehods o Solve he Berh AllocaonProblem wh Sochasc Arrval and Handlng Tmes Nsh Umang* Mchel Berlare* * TRANSP-OR, Ecole Polyechnque Fédérale de Lausanne Frs Workshop on Large Scale Opmzaon November

More information

Let s treat the problem of the response of a system to an applied external force. Again,

Let s treat the problem of the response of a system to an applied external force. Again, Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem

More information

Knowing What Others Know: Coordination Motives in Information Acquisition Additional Notes

Knowing What Others Know: Coordination Motives in Information Acquisition Additional Notes Knowng Wha Ohers Know: Coordnaon Moves n nformaon Acquson Addonal Noes Chrsan Hellwg Unversy of Calforna, Los Angeles Deparmen of Economcs Laura Veldkamp New York Unversy Sern School of Busness March 1,

More information

Supporting information How to concatenate the local attractors of subnetworks in the HPFP

Supporting information How to concatenate the local attractors of subnetworks in the HPFP n Effcen lgorh for Idenfyng Prry Phenoype rcors of Lrge-Scle Boolen Newor Sng-Mo Choo nd Kwng-Hyun Cho Depren of Mhecs Unversy of Ulsn Ulsn 446 Republc of Kore Depren of Bo nd Brn Engneerng Kore dvnced

More information

PHYS 705: Classical Mechanics. Canonical Transformation

PHYS 705: Classical Mechanics. Canonical Transformation PHYS 705: Classcal Mechancs Canoncal Transformaon Canoncal Varables and Hamlonan Formalsm As we have seen, n he Hamlonan Formulaon of Mechancs,, are ndeenden varables n hase sace on eual foong The Hamlon

More information

Political Economy of Institutions and Development: Problem Set 2 Due Date: Thursday, March 15, 2019.

Political Economy of Institutions and Development: Problem Set 2 Due Date: Thursday, March 15, 2019. Polcal Economy of Insuons and Developmen: 14.773 Problem Se 2 Due Dae: Thursday, March 15, 2019. Please answer Quesons 1, 2 and 3. Queson 1 Consder an nfne-horzon dynamc game beween wo groups, an ele and

More information

( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model

( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng

More information

Chapters 2 Kinematics. Position, Distance, Displacement

Chapters 2 Kinematics. Position, Distance, Displacement Chapers Knemacs Poson, Dsance, Dsplacemen Mechancs: Knemacs and Dynamcs. Knemacs deals wh moon, bu s no concerned wh he cause o moon. Dynamcs deals wh he relaonshp beween orce and moon. The word dsplacemen

More information

Oligopoly with exhaustible resource input

Oligopoly with exhaustible resource input Olgopoly wh exhausble resource npu e, P-Y. 78 Olgopoly wh exhausble resource npu Recebmeno dos orgnas: 25/03/202 Aceação para publcação: 3/0/203 Pu-yan e PhD em Scences pela Chnese Academy of Scence Insução:

More information

Optimal environmental charges under imperfect compliance

Optimal environmental charges under imperfect compliance ISSN 1 746-7233, England, UK World Journal of Modellng and Smulaon Vol. 4 (28) No. 2, pp. 131-139 Opmal envronmenal charges under mperfec complance Dajn Lu 1, Ya Wang 2 Tazhou Insue of Scence and Technology,

More information

The safety stock and inventory cost paradox in a stochastic lead time setting

The safety stock and inventory cost paradox in a stochastic lead time setting Dsney, S.M., Malz, A., Wang, X. and Warburon, R., (05), The safey soc and nvenory cos paradox n a sochasc lead me seng, 6 h Producon and Operaons Managemen Socey Annual Conference, Washngon, USA, May 8

More information

On One Analytic Method of. Constructing Program Controls

On One Analytic Method of. Constructing Program Controls Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna

More information

MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES. Institute for Mathematical Research, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia

MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES. Institute for Mathematical Research, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia Malaysan Journal of Mahemacal Scences 9(2): 277-300 (2015) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Journal homeage: h://ensemumedumy/journal A Mehod for Deermnng -Adc Orders of Facorals 1* Rafka Zulkal,

More information

Solutions to exam in SF1811 Optimization, Jan 14, 2015

Solutions to exam in SF1811 Optimization, Jan 14, 2015 Solutons to exam n SF8 Optmzaton, Jan 4, 25 3 3 O------O -4 \ / \ / The network: \/ where all lnks go from left to rght. /\ / \ / \ 6 O------O -5 2 4.(a) Let x = ( x 3, x 4, x 23, x 24 ) T, where the varable

More information

Midterm Exam. Thursday, April hour, 15 minutes

Midterm Exam. Thursday, April hour, 15 minutes Economcs of Grow, ECO560 San Francsco Sae Unvers Mcael Bar Sprng 04 Mderm Exam Tursda, prl 0 our, 5 mnues ame: Insrucons. Ts s closed boo, closed noes exam.. o calculaors of an nd are allowed. 3. Sow all

More information

2 Aggregate demand in partial equilibrium static framework

2 Aggregate demand in partial equilibrium static framework Unversy of Mnnesoa 8107 Macroeconomc Theory, Sprng 2009, Mn 1 Fabrzo Perr Lecure 1. Aggregaon 1 Inroducon Probably so far n he macro sequence you have deal drecly wh represenave consumers and represenave

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

ON THE WEAK LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS

ON THE WEAK LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS ON THE WEA LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS FENGBO HANG Absrac. We denfy all he weak sequenal lms of smooh maps n W (M N). In parcular, hs mples a necessary su cen opologcal

More information

Dual Approximate Dynamic Programming for Large Scale Hydro Valleys

Dual Approximate Dynamic Programming for Large Scale Hydro Valleys Dual Approxmae Dynamc Programmng for Large Scale Hydro Valleys Perre Carpener and Jean-Phlppe Chanceler 1 ENSTA ParsTech and ENPC ParsTech CMM Workshop, January 2016 1 Jon work wh J.-C. Alas, suppored

More information

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5 TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres

More information

A Deza Frankl type theorem for set partitions

A Deza Frankl type theorem for set partitions A Deza Frankl ype heorem for se parons Cheng Yeaw Ku Deparmen of Mahemacs Naonal Unversy of Sngapore Sngapore 117543 makcy@nus.edu.sg Kok Bn Wong Insue of Mahemacal Scences Unversy of Malaya 50603 Kuala

More information

Li An-Ping. Beijing , P.R.China

Li An-Ping. Beijing , P.R.China A New Type of Cpher: DICING_csb L An-Png Bejng 100085, P.R.Chna apl0001@sna.com Absrac: In hs paper, we wll propose a new ype of cpher named DICING_csb, whch s derved from our prevous sream cpher DICING.

More information

Lecture VI Regression

Lecture VI Regression Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4 CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped

More information

The Dynamic Programming Models for Inventory Control System with Time-varying Demand

The Dynamic Programming Models for Inventory Control System with Time-varying Demand The Dynamc Programmng Models for Invenory Conrol Sysem wh Tme-varyng Demand Truong Hong Trnh (Correspondng auhor) The Unversy of Danang, Unversy of Economcs, Venam Tel: 84-236-352-5459 E-mal: rnh.h@due.edu.vn

More information

Efficient Asynchronous Channel Hopping Design for Cognitive Radio Networks

Efficient Asynchronous Channel Hopping Design for Cognitive Radio Networks Effcen Asynchronous Channel Hoppng Desgn for Cognve Rado Neworks Chh-Mn Chao, Chen-Yu Hsu, and Yun-ng Lng Absrac In a cognve rado nework (CRN), a necessary condon for nodes o communcae wh each oher s ha

More information

EXECUTION COSTS IN FINANCIAL MARKETS WITH SEVERAL INSTITUTIONAL INVESTORS

EXECUTION COSTS IN FINANCIAL MARKETS WITH SEVERAL INSTITUTIONAL INVESTORS EXECUION COSS IN FINANCIAL MARKES WIH SEVERAL INSIUIONAL INVESORS Somayeh Moazen, Yuyng L, Kae Larson Cheron School of Compuer Scence Unversy of Waerloo, Waerloo, ON, Canada emal: {smoazen, yuyng, klarson}@uwaerlooca

More information

Part II CONTINUOUS TIME STOCHASTIC PROCESSES

Part II CONTINUOUS TIME STOCHASTIC PROCESSES Par II CONTINUOUS TIME STOCHASTIC PROCESSES 4 Chaper 4 For an advanced analyss of he properes of he Wener process, see: Revus D and Yor M: Connuous marngales and Brownan Moon Karazas I and Shreve S E:

More information

Technical Appendix to The Equivalence of Wage and Price Staggering in Monetary Business Cycle Models

Technical Appendix to The Equivalence of Wage and Price Staggering in Monetary Business Cycle Models Techncal Appendx o The Equvalence of Wage and Prce Saggerng n Moneary Busness Cycle Models Rochelle M. Edge Dvson of Research and Sascs Federal Reserve Board Sepember 24, 2 Absrac Ths appendx deals he

More information

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are Chaper 6 DEECIO AD EIMAIO: Fundamenal ssues n dgal communcaons are. Deecon and. Esmaon Deecon heory: I deals wh he desgn and evaluaon of decson makng processor ha observes he receved sgnal and guesses

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

Chapter 2 Linear dynamic analysis of a structural system

Chapter 2 Linear dynamic analysis of a structural system Chaper Lnear dynamc analyss of a srucural sysem. Dynamc equlbrum he dynamc equlbrum analyss of a srucure s he mos general case ha can be suded as akes no accoun all he forces acng on. When he exernal loads

More information

10. A.C CIRCUITS. Theoretically current grows to maximum value after infinite time. But practically it grows to maximum after 5τ. Decay of current :

10. A.C CIRCUITS. Theoretically current grows to maximum value after infinite time. But practically it grows to maximum after 5τ. Decay of current : . A. IUITS Synopss : GOWTH OF UNT IN IUIT : d. When swch S s closed a =; = d. A me, curren = e 3. The consan / has dmensons of me and s called he nducve me consan ( τ ) of he crcu. 4. = τ; =.63, n one

More information

How about the more general "linear" scalar functions of scalars (i.e., a 1st degree polynomial of the following form with a constant term )?

How about the more general linear scalar functions of scalars (i.e., a 1st degree polynomial of the following form with a constant term )? lmcd Lnear ransformaon of a vecor he deas presened here are que general hey go beyond he radonal mar-vecor ype seen n lnear algebra Furhermore, hey do no deal wh bass and are equally vald for any se of

More information

A Tour of Modeling Techniques

A Tour of Modeling Techniques A Tour of Modelng Technques John Hooker Carnege Mellon Unversy EWO Semnar February 8 Slde Oulne Med neger lnear (MILP) modelng Dsuncve modelng Knapsack modelng Consran programmng models Inegraed Models

More information

CONSISTENT ESTIMATION OF THE NUMBER OF DYNAMIC FACTORS IN A LARGE N AND T PANEL. Detailed Appendix

CONSISTENT ESTIMATION OF THE NUMBER OF DYNAMIC FACTORS IN A LARGE N AND T PANEL. Detailed Appendix COSISE ESIMAIO OF HE UMBER OF DYAMIC FACORS I A LARGE AD PAEL Dealed Aendx July 005 hs verson: May 9, 006 Dane Amengual Dearmen of Economcs, Prnceon Unversy and Mar W Wason* Woodrow Wlson School and Dearmen

More information

On the numerical treatment ofthenonlinear partial differentialequation of fractional order

On the numerical treatment ofthenonlinear partial differentialequation of fractional order IOSR Journal of Mahemacs (IOSR-JM) e-iss: 2278-5728, p-iss: 239-765X. Volume 2, Issue 6 Ver. I (ov. - Dec.26), PP 28-37 www.osrjournals.org On he numercal reamen ofhenonlnear paral dfferenalequaon of fraconal

More information

Existence of Time Periodic Solutions for the Ginzburg-Landau Equations. model of superconductivity

Existence of Time Periodic Solutions for the Ginzburg-Landau Equations. model of superconductivity Journal of Mahemacal Analyss and Applcaons 3, 3944 999 Arcle ID jmaa.999.683, avalable onlne a hp:www.dealbrary.com on Exsence of me Perodc Soluons for he Gnzburg-Landau Equaons of Superconducvy Bxang

More information

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information

FTCS Solution to the Heat Equation

FTCS Solution to the Heat Equation FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence

More information

# c i. INFERENCE FOR CONTRASTS (Chapter 4) It's unbiased: Recall: A contrast is a linear combination of effects with coefficients summing to zero:

# c i. INFERENCE FOR CONTRASTS (Chapter 4) It's unbiased: Recall: A contrast is a linear combination of effects with coefficients summing to zero: 1 INFERENCE FOR CONTRASTS (Chapter 4 Recall: A contrast s a lnear combnaton of effects wth coeffcents summng to zero: " where " = 0. Specfc types of contrasts of nterest nclude: Dfferences n effects Dfferences

More information

Demographics in Dynamic Heckscher-Ohlin Models: Overlapping Generations versus Infinitely Lived Consumers*

Demographics in Dynamic Heckscher-Ohlin Models: Overlapping Generations versus Infinitely Lived Consumers* Federal Reserve Ban of Mnneapols Research Deparmen Saff Repor 377 Sepember 6 Demographcs n Dynamc Hecscher-Ohln Models: Overlappng Generaons versus Infnely Lved Consumers* Clausre Bajona Unversy of Mam

More information

Multi-priority Online Scheduling with Cancellations

Multi-priority Online Scheduling with Cancellations Submed o Operaons Research manuscrp (Please, provde he manuscrp number!) Auhors are encouraged o subm new papers o INFORMS journals by means of a syle fle emplae, whch ncludes he journal le. However, use

More information

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm H ( q, p, ) = q p L( q, q, ) H p = q H q = p H = L Equvalen o Lagrangan formalsm Smpler, bu

More information

EEL 6266 Power System Operation and Control. Chapter 5 Unit Commitment

EEL 6266 Power System Operation and Control. Chapter 5 Unit Commitment EEL 6266 Power Sysem Operaon and Conrol Chaper 5 Un Commmen Dynamc programmng chef advanage over enumeraon schemes s he reducon n he dmensonaly of he problem n a src prory order scheme, here are only N

More information

General viscosity iterative method for a sequence of quasi-nonexpansive mappings

General viscosity iterative method for a sequence of quasi-nonexpansive mappings Avalable onlne at www.tjnsa.com J. Nonlnear Sc. Appl. 9 (2016), 5672 5682 Research Artcle General vscosty teratve method for a sequence of quas-nonexpansve mappngs Cuje Zhang, Ynan Wang College of Scence,

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm Hqp (,,) = qp Lqq (,,) H p = q H q = p H L = Equvalen o Lagrangan formalsm Smpler, bu wce as

More information