Duality in linear programming

Size: px
Start display at page:

Download "Duality in linear programming"

Transcription

1 MPRA Munch Personal RePEc Archve Dualty n lnear programmng Mhaela Albc and Dela Teselos and Raluca Prundeanu and Ionela Popa Unversty Constantn Brancoveanu Ramncu Valcea 7 January 00 Onlne at MPRA Paper No 986, posted 9 January 00 :58 UTC

2 DUALITY IN LINEAR PROGRAMMING Albc Mhaela, Unversty Constantn Brancoveanu, Rm Valcea Teselos Dela, Unversty Constantn Brancoveanu, Ptest Prundeanu Raluca, Unversty Constantn Brancoveanu, Rm Valcea Popa Ionela, Unversty Constantn Brancoveanu, Rm Valcea Abstract Any lnear programmng problem marked as P and called prmal can be seen n connecton th another lnear programmng problem marked as D and called dual The economc nterpretaton of the dual model brngs about ne nformaton hen analyzng such phenomena and hen substantatng decson makng JEL classfcaton: C60, C6 Keyords: Lnear programmng problem, dualty The dea of a lnear programme s dualty and the theory of lnear programmng along th the dualty markng manner have played a specal role n economc analyses by the ay n hch they have emphaszed the nature of prces Ever snce margnal analyss onards, no other dea has proven to be that mportant to the fundamental theory of prces Dual Problem Let the eample of lnear programmng n ts general form be : mn n = n = n = n ( ma ) f = a a a b b = b 0, = n = c, = k, = k p, = p m We shall call ths problem «prmal» and mark t as P The prmal problem can be assocated th another lnear programmng problem marked as D and called «dual» The transton from the prmal problem to the dual one s done accordng to the follong rules : before the transton takes place, the prmal problem must be turned nto ts canoncal form ; f the prmal lnear programmng problem s mamum, then the dual lnear programmng one s mnmum and the other ay round ; Lancaster K (97) Mathematcal Economc Analyss, Scentfc Publshng House, Bucharest

3 the number of restrctons n the prmal lnear programmng problem equals the number of varables n the dual lnear programmng problem ; the number of varables n the prmal lnear programmng problem equals the number of restrctons n the dual lnear programmng problem ; 5 vector c n the prmal lnear programmng problem s vector "b n the dual lnear programmng problem ; 6 vector b n the prmal lnear programmng problem s vector «c» n the dual lnear programmng problem ; 7 the factors matr n the dual lnear programmng problem s the transposed matr of the prmal lnear programmng problem Observaton The dualty relaton s symmetrc: the dualty s dualty s the prmal problem Correspondence rules beteen the prmal lnear programmng problem and the dual lnear programmng one []: LPP P mnmum mamum number of varables number of restrctons free terms of restrctons factors of obectve functon columns of restrctons matr varable 0 varable 0 varable R harmonous restrcton non-harmonous restrcton equalty restrcton LPP D mamum mnmum number of restrctons number of varables factors of obectve functon free terms of restrctons ros of restrctons matr harmonous restrcton non-harmonous restrcton equalty restrcton varable 0 varable 0 varable R Let the lnear programmng problem be: mn f ( ) = c A b (P) 0 Its dual nature s to be the lnear programmng problem: ma g( ) = b A c (D) 0 Analogous as to the lnear programmng problem n a mamum canoncal form: ma f ( ) = c A b (P) 0 Its dual nature s to be: mn g( ) = b A c (D) 0 Eample Let us rte the dual nature of the lnear programmng problem: ma f = ( )

4 ,, 0 = The problem s canoncal form s: ( ) ma f =,, 0 = The problem s dual nature s to be: ( ) mn g = 0, 0 Eample Let us rte the dual nature of the lnear programmng problem: ( ) mn f = 0 0 The problem s turned back nto ts canoncal form: ( ) mn f = 0 0 The problem s dual nature s: ( ) ma g =,, 0 = It s mportant to understand that dualty s frst and foremost a formal mathematcal relaton Once a problem has been suggested, one makes up ts double nature accordng to the rules above If the prmal problem s consstent, one naturally epects ts dualty to prove nterestng One s epectaton can be grounded or not, but dualty s presence as a formal feature of the prmal problem s not affected []

5 Dualty Theorems Heren are the dualty theorems that sho the connecton beteen the prmal problem and the dual problem n a canoncal form Let the couple of problems be: mn f ( ) = c ma g( ) = b (P) A b (D) A c 0 0 A M m, n, M n,, M, m Let P = { 0, A b} and P = { 0, A c} be the set of admssble solutons of prmal lnear programmng problem (P), and of dual lnear programmng problem (D), respectvely Proposton Irrespectve of hat P and solutons of the to problems, there s the follong nequalty: g( ) f ( ) dec b c Demonstraton: P A b P are a ( ), couple of The relaton s multpled by on the left and the result s A b P hence A c The relaton s multpled by on the rght and the result s A c Therefore, b A c Proposton If soluton couple ( ~, ~ ) of the to problems has the feature that g ( ~ ) = f ( ~ ), then ~ s the optmal soluton of (P) prmal lnear programmng problem and ~ s the best soluton of (D) dual lnear programmng one Demonstraton: We suppose by reducto ad absurdum that ~ s not the optmal soluton of (P) prmal lnear programmng problem; then, there s soluton * P so that ( * f ) < f ( ~ ) ( (P) prmal lnear programmng problem beng mnmum) But f ( ~ ) = g ( ~ ) ( ) ( ) ( ) * f < f ~ = g ~ *, so there s couple (, ~ ) of admssble solutons that contradcts Proposton Corollary ) If (P) prmal lnear programmng problem does not have fnte optmzaton, then (D) dual lnear programmng problem does not have any admssble solutons (namely P = Φ); ) If (D) dual lnear programmng problem does not have fnte optmzaton, then (P) prmal lnear programmng problem does not have any admssble solutons (namely P = Φ) Theorem 5 If the soluton of the (P) prmal lnear programmng problem s ( ~ P ) and fnte, then the best soluton of the (D) dual lnear programmng problem s stll ( ~ P ) and fnte, and the optmal values of the obectve functons concde: f ( ~ ) = g ( ~ ) It s ntutvely deduced from Propostons, and Corollary by negaton Addtonally, f ~ s the basc optmal soluton of the (P) prmal lnear programmng problem for base B ~ made up th m ndependent lnear column vectors n A = ( a, a,, a,, am ), then ~ ~ B ~ ~ ~ ~ = = B b ; = c ~ B B here c ~ B are the m costs correspondng to the vectors n base B ~ The values of the obectve functons are: f ( ~ ~ ) = c ~ B b B ş g( ~ ~ ) = c ~ B b B hence f ( ~ ) = g ( ~ )

6 Ths theorem leads to the concluson that the fnal smple table correspondng to the prmal problem ncludes the optmal solutons of both problems (prmal and dual) The soluton T B = cb B of the dual problem s obtaned on ro z at the cross th the vectors columns that have formed the orgnal base Analogously, f the dual problem s solved, the result s that the soluton of the (P) prmal lnear programmng problem s to be found n the last smple table of the (D) dual B lnear programmng problem, on ro z c, ust belo the columns that have orgnally formed the base Ths consequence gves the possblty to solve a (P) prmal lnear programmng problem by ts dual one f the latter s easer to solve, and the solutons of the prmal one are read accordng to the above Theorem 6 (The Fundamental Theorem of Dualty) For any couple of dual problems, one and only one of the follong stuatons s possble: ) Both problems have solutons: therefore, they have optmal solutons and the optmal values of the obectve functons concde; ) One of the problems has a soluton, the other does not: therefore, the former problem has fnte optmzaton; ) Nether of the to problems has a soluton Theorem 7 (The Theorem of Complementary Spacng) Takng account of the couple of lnear programmng problems (P), (D) stated above, the maor and suffcent condton for ~ solutons P and P ~ to be optmal s: ~ ( A ~ b) = 0 ( c A ~ ) ~ = 0 Demonstraton: In order to demonstrate emergency, let ~ and ~ be the optmal solutons of the dual A ~ b A ~ c problems, namely ~ and 0 ~ 0 Then ~ ( A ~ b) 0 and ( c A ~ ) ~ 0 But c ~ = ~ b, c ~ A ~ ~ = b ~ A ~ ~, ( c A ~ ) ~ ~ ( A ~ b) = 0 Snce the to addton terms n the left member of the obtaned nequalty are nonnegatve, the result s that ether s nule and therefore pursues the desred condtons For suffcency, f these relatons are added: ~ ( A ~ b) = 0 ( c A ~ ) ~, = 0 the result s: ~ A ~ b ~ c ~ A ~ ~ = 0 c ~ = ~ b and so ~ s the optmal soluton of the prmal problem, and ~ s the best soluton of the dual problem, accordng to Proposton Lemma 8 (The Fundamental Lemma) If and are possble vectors of the prmal, respectvely the dual problem, the follong relatons are true: c A b Demonstraton: It s notced that A b 0 s obtaned from the prmal problem s restrctons Snce 0 f s possble, ( A b) 0 Hence, A b Usng the dual problem s restrctons A c 0 and the non-negatvty restrctons upon, there s, f and are possble: c A 5

7 Theorem 9 (The Equlbrum Theorem of Lnear Programmng) a) If, are possble ponts for the prmal and dual problem, they are optmal f and only f: () = 0 henever a < b ; () = 0 henever a > c, that s the k-th varable of a problem s nule hen the k-th restrcton of the other one s not effectve b) The optmal pont (or optmal ponts f t s about beng non-strctly optmal) shall be so that the number of non-nule varables of ether problem shall not eceed the number of restrctons n that problem The equlbrum theorem s mportant for to reasons Frstly, t allos one to verfy a prmal soluton s ablty to be optmal even f one does not have the optmal dual soluton Then, even more remarkably, t leads one to a number of nterpretatons of economc models condtons to be optmal, models havng the eact form requred by lnear programmng Economc Interpretaton Let us consder a lnear manufacturng model th n outputs and m b nputs beteen hch there s a relaton defned by a constant manufacturng factors The factors sho hat nput amount s necessary to manufacture a output unt In ths case, a s the total nput amount necessary for the manufacturng of compound output A means vector b of the nputs necessary to manufacture ths compound output Vector p of products prces and vector b of all avalable resources are stated The optmal manufacturng s defned as beng ma p and ts features are analyzed Therefore, there s a lnear programmng problem ma p Its dual problem s then A b 0 mn b A p 0 It s knon from the dualty theorem that p = b, p s a value epresson (prces multpled by amounts); therefore, t s epected that b have the same meanng Snce b s the amount vector, s a random vector of prces hch n ths case are nputs prces Due to the ne dmenson that dual varables get by ther connecton th prces n economc matters, dual varables shall be often knon as shado prces Let us no consder the dual problem s restrctons, each havng the follong form: Snce a p a s nput amount necessary to manufacture a output amount, a a s the represents the value of nput necessary to manufacture a output unt, and Lancaster K (97) Mathematcal Economc Analyss, Scentfc Publshng House, Bucharest 6

8 total value of nputs necessary for the manufacturng of a output unt, all nputs beng assessed n shado prces The anser to ths queston s pursued: hch s the loest value that s to be attached to the vector of b avalable resources knong that there s a possblty to turn resources nto products and then to sell them? The restrctons of the dual problem epress the fact that f the value of the nputs ncorporated n a product s loer than the product s prce, t s more advantageous to sell the products nstead of the resources Once the, optmal ponts have been reached, the economy (or the enterprse) does no longer care f t sells the product obtanng p, or f t sells the resources at prces, because the total cashng s the same: p = b Thus, t can be stated that: any resource that cannot be entrely used for the manufacturng of an optmal compound output shall be gven a shado prce equallng zero or t shall be consdered that ts optmal value s nule; once an optmal state has been acheved, no product shall be seen as such f ts unt cost eceeds ts prce (the nputs beng assessed by optmal shado prces) In other ords, the resources that make up the ecess supply are free goods and the manufacturng generatng losses shall be left out n case the shado prces are real ones These relatons correspond to the equlbrum of a compettve economy If only the -th restrcton vares, t s deduced that the -th dual varable (n the optmal pont) can be consdered the margnal value of the problem modfyng the -th restrcton In typcal economc contets, there s gong to be the margnal socal value (or margnal revenue) of the ncrease n a proper resource amount Thus, one can ustfy the common nterpretaton of dual varables as shado prces Eample Let the lnear programmng problem be: ( ) = 5 ma f 0, =,, ) State the optmal soluton by usng prmal smple algorthm; ) Wrte the dual problem assocated th the one above and then rte ts optmal soluton; ) Interpret the dual problem s solutons from the economc pont of ve ) The lnear programmng problem s brought back to ts standard form: ma f ( ) = 5 0( y y ) y = y = 0, =,, y, y 0 0 If the optmal state s not unque, the last statement s vald for at least one optmal pont 7

9 0 B C B B θmn y y y 0-0 y 0 0 / y 5/ / 0 -/ -/ 5/ 5 / / / 0 / 5/ -/ 0 9/ 0 5/ y Snce the problem s mamum, all the problem s optmal soluton s: =, = 0, = 0, y =, y = 0 Products P and P are not manufactured - they are not effcent The mamum proft s b) The dual problem of the orgnal one s: m n g( ) = 5, 0 The dual problem s solutons are: =, =, y = 9, y =, y 0 = c) mn g( ) = Product P s manufactured n the amount and resources y ş y reman unbought References Bădn V, Frcă O, (995) Mathematcs Course for Economsts, The Romanan- Amercan Unversty, Bucharest Dumtru M, Manole S (coord) (006) Economc Mathematcs, Economc Independence Publshng House, Pteşt Lancaster K (97) Mathematcal Economc Analyss, Scentfc Publshng House, Bucharest 0 8

10 Oprescu Gh (coord) (999) Economcs Appled Mathematcs, Independence Publshng House, Brăla Economc 9

Module 9. Lecture 6. Duality in Assignment Problems

Module 9. Lecture 6. Duality in Assignment Problems Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept

More information

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009 College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:

More information

COS 521: Advanced Algorithms Game Theory and Linear Programming

COS 521: Advanced Algorithms Game Theory and Linear Programming COS 521: Advanced Algorthms Game Theory and Lnear Programmng Moses Charkar February 27, 2013 In these notes, we ntroduce some basc concepts n game theory and lnear programmng (LP). We show a connecton

More information

12. The Hamilton-Jacobi Equation Michael Fowler

12. The Hamilton-Jacobi Equation Michael Fowler 1. The Hamlton-Jacob Equaton Mchael Fowler Back to Confguraton Space We ve establshed that the acton, regarded as a functon of ts coordnate endponts and tme, satsfes ( ) ( ) S q, t / t+ H qpt,, = 0, and

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner 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 information

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

More information

Support Vector Machines CS434

Support Vector Machines CS434 Support Vector Machnes CS434 Lnear Separators Many lnear separators exst that perfectly classfy all tranng examples Whch of the lnear separators s the best? Intuton of Margn Consder ponts A, B, and C We

More information

Assortment Optimization under MNL

Assortment 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 information

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg

princeton 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 information

Perfect Competition and the Nash Bargaining Solution

Perfect 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 information

Computing Correlated Equilibria in Multi-Player Games

Computing Correlated Equilibria in Multi-Player Games Computng Correlated Equlbra n Mult-Player Games Chrstos H. Papadmtrou Presented by Zhanxang Huang December 7th, 2005 1 The Author Dr. Chrstos H. Papadmtrou CS professor at UC Berkley (taught at Harvard,

More information

Pattern Classification

Pattern Classification Pattern Classfcaton All materals n these sldes ere taken from Pattern Classfcaton (nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wley & Sons, 000 th the permsson of the authors and the publsher

More information

Welfare Properties of General Equilibrium. What can be said about optimality properties of resource allocation implied by general equilibrium?

Welfare 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 information

The Gaussian classifier. Nuno Vasconcelos ECE Department, UCSD

The Gaussian classifier. Nuno Vasconcelos ECE Department, UCSD he Gaussan classfer Nuno Vasconcelos ECE Department, UCSD Bayesan decson theory recall that we have state of the world X observatons g decson functon L[g,y] loss of predctng y wth g Bayes decson rule s

More information

Hopfield Training Rules 1 N

Hopfield Training Rules 1 N Hopfeld Tranng Rules To memorse a sngle pattern Suppose e set the eghts thus - = p p here, s the eght beteen nodes & s the number of nodes n the netor p s the value requred for the -th node What ll the

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

Game Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India February 2008

Game Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India February 2008 Game Theory Lecture Notes By Y. Narahar Department of Computer Scence and Automaton Indan Insttute of Scence Bangalore, Inda February 2008 Chapter 10: Two Person Zero Sum Games Note: Ths s a only a draft

More information

CHAPTER-5 INFORMATION MEASURE OF FUZZY MATRIX AND FUZZY BINARY RELATION

CHAPTER-5 INFORMATION MEASURE OF FUZZY MATRIX AND FUZZY BINARY RELATION CAPTER- INFORMATION MEASURE OF FUZZY MATRI AN FUZZY BINARY RELATION Introducton The basc concept of the fuzz matr theor s ver smple and can be appled to socal and natural stuatons A branch of fuzz matr

More information

The Minimum Universal Cost Flow in an Infeasible Flow Network

The 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 information

b ), which stands for uniform distribution on the interval a x< b. = 0 elsewhere

b ), which stands for uniform distribution on the interval a x< b. = 0 elsewhere Fall Analyss of Epermental Measurements B. Esensten/rev. S. Errede Some mportant probablty dstrbutons: Unform Bnomal Posson Gaussan/ormal The Unform dstrbuton s often called U( a, b ), hch stands for unform

More information

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise.

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise. Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where y + = β + β e for =,..., y and are observable varables e s a random error How can an estmaton rule be constructed for the

More information

1 GSW Iterative Techniques for y = Ax

1 GSW Iterative Techniques for y = Ax 1 for y = A I m gong to cheat here. here are a lot of teratve technques that can be used to solve the general case of a set of smultaneous equatons (wrtten n the matr form as y = A), but ths chapter sn

More information

e i is a random error

e i is a random error Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where + β + β e for,..., and are observable varables e s a random error How can an estmaton rule be constructed for the unknown

More information

Which Separator? Spring 1

Which Separator? Spring 1 Whch Separator? 6.034 - Sprng 1 Whch Separator? Mamze the margn to closest ponts 6.034 - Sprng Whch Separator? Mamze the margn to closest ponts 6.034 - Sprng 3 Margn of a pont " # y (w $ + b) proportonal

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

On the Multicriteria Integer Network Flow Problem

On 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 information

,, MRTS is the marginal rate of technical substitution

,, MRTS is the marginal rate of technical substitution Mscellaneous Notes on roducton Economcs ompled by eter F Orazem September 9, 00 I Implcatons of conve soquants Two nput case, along an soquant 0 along an soquant Slope of the soquant,, MRTS s the margnal

More information

15-381: Artificial Intelligence. Regression and cross validation

15-381: Artificial Intelligence. Regression and cross validation 15-381: Artfcal Intellgence Regresson and cross valdaton Where e are Inputs Densty Estmator Probablty Inputs Classfer Predct category Inputs Regressor Predct real no. Today Lnear regresson Gven an nput

More information

Portfolios with Trading Constraints and Payout Restrictions

Portfolios with Trading Constraints and Payout Restrictions Portfolos wth Tradng Constrants and Payout Restrctons John R. Brge Northwestern Unversty (ont wor wth Chrs Donohue Xaodong Xu and Gongyun Zhao) 1 General Problem (Very) long-term nvestor (eample: unversty

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

Risks and Uncertainties in Agriculture

Risks and Uncertainties in Agriculture Volume, Issue () ISS: -89 Rsks and Uncertantes n Agrculture Isabella SIMA Camela MARI Constantn Brancoveanu Unversty Faculty of Management Marketng n Economc Affars Ptest, Romana _onescu@yahoo.com Constantn

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 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 information

10) Activity analysis

10) Activity analysis 3C3 Mathematcal Methods for Economsts (6 cr) 1) Actvty analyss Abolfazl Keshvar Ph.D. Aalto Unversty School of Busness Sldes orgnally by: Tmo Kuosmanen Updated by: Abolfazl Keshvar 1 Outlne Hstorcal development

More information

3.1 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

3.1 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 information

MMA and GCMMA two methods for nonlinear optimization

MMA 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 information

Some modelling aspects for the Matlab implementation of MMA

Some 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 information

Interactive Bi-Level Multi-Objective Integer. Non-linear Programming Problem

Interactive Bi-Level Multi-Objective Integer. Non-linear Programming Problem Appled Mathematcal Scences Vol 5 0 no 65 3 33 Interactve B-Level Mult-Objectve Integer Non-lnear Programmng Problem O E Emam Department of Informaton Systems aculty of Computer Scence and nformaton Helwan

More information

x i1 =1 for all i (the constant ).

x i1 =1 for all i (the constant ). Chapter 5 The Multple Regresson Model Consder an economc model where the dependent varable s a functon of K explanatory varables. The economc model has the form: y = f ( x,x,..., ) xk Approxmate ths by

More information

Lecture 21: Numerical methods for pricing American type derivatives

Lecture 21: Numerical methods for pricing American type derivatives Lecture 21: Numercal methods for prcng Amercan type dervatves Xaoguang Wang STAT 598W Aprl 10th, 2014 (STAT 598W) Lecture 21 1 / 26 Outlne 1 Fnte Dfference Method Explct Method Penalty Method (STAT 598W)

More information

Lagrange Multipliers Kernel Trick

Lagrange Multipliers Kernel Trick Lagrange Multplers Kernel Trck Ncholas Ruozz Unversty of Texas at Dallas Based roughly on the sldes of Davd Sontag General Optmzaton A mathematcal detour, we ll come back to SVMs soon! subject to: f x

More information

Multigradient for Neural Networks for Equalizers 1

Multigradient for Neural Networks for Equalizers 1 Multgradent for Neural Netorks for Equalzers 1 Chulhee ee, Jnook Go and Heeyoung Km Department of Electrcal and Electronc Engneerng Yonse Unversty 134 Shnchon-Dong, Seodaemun-Ku, Seoul 1-749, Korea ABSTRACT

More information

6.854J / J Advanced Algorithms Fall 2008

6.854J / J Advanced Algorithms Fall 2008 MIT OpenCourseWare http://ocw.mt.edu 6.854J / 18.415J Advanced Algorthms Fall 2008 For nformaton about ctng these materals or our Terms of Use, vst: http://ocw.mt.edu/terms. 18.415/6.854 Advanced Algorthms

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have

More information

Convexity preserving interpolation by splines of arbitrary degree

Convexity 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 information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

SL n (F ) Equals its Own Derived Group

SL n (F ) Equals its Own Derived Group Internatonal Journal of Algebra, Vol. 2, 2008, no. 12, 585-594 SL n (F ) Equals ts Own Derved Group Jorge Macel BMCC-The Cty Unversty of New York, CUNY 199 Chambers street, New York, NY 10007, USA macel@cms.nyu.edu

More information

2.3 Nilpotent endomorphisms

2.3 Nilpotent endomorphisms s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms

More information

Neryškioji dichotominių testo klausimų ir socialinių rodiklių diferencijavimo savybių klasifikacija

Neryškioji dichotominių testo klausimų ir socialinių rodiklių diferencijavimo savybių klasifikacija Neryškoj dchotomnų testo klausmų r socalnų rodklų dferencjavmo savybų klasfkacja Aleksandras KRYLOVAS, Natalja KOSAREVA, Julja KARALIŪNAITĖ Technologcal and Economc Development of Economy Receved 9 May

More information

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M CIS56: achne Learnng Lecture 3 (Sept 6, 003) Preparaton help: Xaoyng Huang Lnear Regresson Lnear regresson can be represented by a functonal form: f(; θ) = θ 0 0 +θ + + θ = θ = 0 ote: 0 s a dummy attrbute

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Difference Equations

Difference 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 information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017 U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES

VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES BÂRZĂ, Slvu Faculty of Mathematcs-Informatcs Spru Haret Unversty barza_slvu@yahoo.com Abstract Ths paper wants to contnue

More information

Discontinuous Extraction of a Nonrenewable Resource

Discontinuous Extraction of a Nonrenewable Resource Dscontnuous Extracton of a Nonrenewable Resource Erc Iksoon Im 1 Professor of Economcs Department of Economcs, Unversty of Hawa at Hlo, Hlo, Hawa Uayant hakravorty Professor of Economcs Department of Economcs,

More information

Discriminative classifier: Logistic Regression. CS534-Machine Learning

Discriminative classifier: Logistic Regression. CS534-Machine Learning Dscrmnatve classfer: Logstc Regresson CS534-Machne Learnng 2 Logstc Regresson Gven tranng set D stc regresson learns the condtonal dstrbuton We ll assume onl to classes and a parametrc form for here s

More information

Norm Bounds for a Transformed Activity Level. Vector in Sraffian Systems: A Dual Exercise

Norm Bounds for a Transformed Activity Level. Vector in Sraffian Systems: A Dual Exercise ppled Mathematcal Scences, Vol. 4, 200, no. 60, 2955-296 Norm Bounds for a ransformed ctvty Level Vector n Sraffan Systems: Dual Exercse Nkolaos Rodousaks Department of Publc dmnstraton, Panteon Unversty

More information

A New Refinement of Jacobi Method for Solution of Linear System Equations AX=b

A New Refinement of Jacobi Method for Solution of Linear System Equations AX=b Int J Contemp Math Scences, Vol 3, 28, no 17, 819-827 A New Refnement of Jacob Method for Soluton of Lnear System Equatons AX=b F Naem Dafchah Department of Mathematcs, Faculty of Scences Unversty of Gulan,

More information

ECE 534: Elements of Information Theory. Solutions to Midterm Exam (Spring 2006)

ECE 534: Elements of Information Theory. Solutions to Midterm Exam (Spring 2006) ECE 534: Elements of Informaton Theory Solutons to Mdterm Eam (Sprng 6) Problem [ pts.] A dscrete memoryless source has an alphabet of three letters,, =,, 3, wth probabltes.4,.4, and., respectvely. (a)

More information

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton

More information

Lecture 4: Universal Hash Functions/Streaming Cont d

Lecture 4: Universal Hash Functions/Streaming Cont d CSE 5: Desgn and Analyss of Algorthms I Sprng 06 Lecture 4: Unversal Hash Functons/Streamng Cont d Lecturer: Shayan Oves Gharan Aprl 6th Scrbe: Jacob Schreber Dsclamer: These notes have not been subjected

More information

Limited Dependent Variables

Limited Dependent Variables Lmted Dependent Varables. What f the left-hand sde varable s not a contnuous thng spread from mnus nfnty to plus nfnty? That s, gven a model = f (, β, ε, where a. s bounded below at zero, such as wages

More information

Affine transformations and convexity

Affine 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 information

Economics 101. Lecture 4 - Equilibrium and Efficiency

Economics 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 information

Convex Optimization. Optimality conditions. (EE227BT: UC Berkeley) Lecture 9 (Optimality; Conic duality) 9/25/14. Laurent El Ghaoui.

Convex Optimization. Optimality conditions. (EE227BT: UC Berkeley) Lecture 9 (Optimality; Conic duality) 9/25/14. Laurent El Ghaoui. Convex Optmzaton (EE227BT: UC Berkeley) Lecture 9 (Optmalty; Conc dualty) 9/25/14 Laurent El Ghaou Organsatonal Mdterm: 10/7/14 (1.5 hours, n class, double-sded cheat sheet allowed) Project: Intal proposal

More information

Discriminative classifier: Logistic Regression. CS534-Machine Learning

Discriminative classifier: Logistic Regression. CS534-Machine Learning Dscrmnatve classfer: Logstc Regresson CS534-Machne Learnng robablstc Classfer Gven an nstance, hat does a probablstc classfer do dfferentl compared to, sa, perceptron? It does not drectl predct Instead,

More information

THE ARIMOTO-BLAHUT ALGORITHM FOR COMPUTATION OF CHANNEL CAPACITY. William A. Pearlman. References: S. Arimoto - IEEE Trans. Inform. Thy., Jan.

THE ARIMOTO-BLAHUT ALGORITHM FOR COMPUTATION OF CHANNEL CAPACITY. William A. Pearlman. References: S. Arimoto - IEEE Trans. Inform. Thy., Jan. THE ARIMOTO-BLAHUT ALGORITHM FOR COMPUTATION OF CHANNEL CAPACITY Wllam A. Pearlman 2002 References: S. Armoto - IEEE Trans. Inform. Thy., Jan. 1972 R. Blahut - IEEE Trans. Inform. Thy., July 1972 Recall

More information

Perron Vectors of an Irreducible Nonnegative Interval Matrix

Perron 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 information

f(x,y) = (4(x 2 4)x,2y) = 0 H(x,y) =

f(x,y) = (4(x 2 4)x,2y) = 0 H(x,y) = Problem Set 3: Unconstraned mzaton n R N. () Fnd all crtcal ponts of f(x,y) (x 4) +y and show whch are ma and whch are mnma. () Fnd all crtcal ponts of f(x,y) (y x ) x and show whch are ma and whch are

More information

LECTURE 9 CANONICAL CORRELATION ANALYSIS

LECTURE 9 CANONICAL CORRELATION ANALYSIS LECURE 9 CANONICAL CORRELAION ANALYSIS Introducton he concept of canoncal correlaton arses when we want to quantfy the assocatons between two sets of varables. For example, suppose that the frst set of

More information

k t+1 + c t A t k t, t=0

k t+1 + c t A t k t, t=0 Macro II (UC3M, MA/PhD Econ) Professor: Matthas Kredler Fnal Exam 6 May 208 You have 50 mnutes to complete the exam There are 80 ponts n total The exam has 4 pages If somethng n the queston s unclear,

More information

Maximal Margin Classifier

Maximal Margin Classifier CS81B/Stat41B: Advanced Topcs n Learnng & Decson Makng Mamal Margn Classfer Lecturer: Mchael Jordan Scrbes: Jana van Greunen Corrected verson - /1/004 1 References/Recommended Readng 1.1 Webstes www.kernel-machnes.org

More information

ALGORITHM FOR THE CALCULATION OF THE TWO VARIABLES CUBIC SPLINE FUNCTION

ALGORITHM FOR THE CALCULATION OF THE TWO VARIABLES CUBIC SPLINE FUNCTION ANALELE ŞTIINŢIFICE ALE UNIVERSITĂŢII AL.I. CUZA DIN IAŞI (S.N.) MATEMATICĂ, Tomul LIX, 013, f.1 DOI: 10.478/v10157-01-00-y ALGORITHM FOR THE CALCULATION OF THE TWO VARIABLES CUBIC SPLINE FUNCTION BY ION

More information

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan

Winter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan Wnter 2008 CS567 Stochastc Lnear/Integer Programmng Guest Lecturer: Xu, Huan Class 2: More Modelng Examples 1 Capacty Expanson Capacty expanson models optmal choces of the tmng and levels of nvestments

More information

MIMA Group. Chapter 2 Bayesian Decision Theory. School of Computer Science and Technology, Shandong University. Xin-Shun SDU

MIMA Group. Chapter 2 Bayesian Decision Theory. School of Computer Science and Technology, Shandong University. Xin-Shun SDU Group M D L M Chapter Bayesan Decson heory Xn-Shun Xu @ SDU School of Computer Scence and echnology, Shandong Unversty Bayesan Decson heory Bayesan decson theory s a statstcal approach to data mnng/pattern

More information

Lecture 10: Euler s Equations for Multivariable

Lecture 10: Euler s Equations for Multivariable Lecture 0: Euler s Equatons for Multvarable Problems Let s say we re tryng to mnmze an ntegral of the form: {,,,,,, ; } J f y y y y y y d We can start by wrtng each of the y s as we dd before: y (, ) (

More information

Salmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2

Salmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2 Salmon: Lectures on partal dfferental equatons 5. Classfcaton of second-order equatons There are general methods for classfyng hgher-order partal dfferental equatons. One s very general (applyng even to

More information

University of California, Davis Date: June 22, 2009 Department of Agricultural and Resource Economics. PRELIMINARY EXAMINATION FOR THE Ph.D.

University of California, Davis Date: June 22, 2009 Department of Agricultural and Resource Economics. PRELIMINARY EXAMINATION FOR THE Ph.D. Unversty of Calforna, Davs Date: June 22, 29 Department of Agrcultural and Resource Economcs Department of Economcs Tme: 5 hours Mcroeconomcs Readng Tme: 2 mnutes PRELIMIARY EXAMIATIO FOR THE Ph.D. DEGREE

More information

Lecture 6: Introduction to Linear Regression

Lecture 6: Introduction to Linear Regression Lecture 6: Introducton to Lnear Regresson An Manchakul amancha@jhsph.edu 24 Aprl 27 Lnear regresson: man dea Lnear regresson can be used to study an outcome as a lnear functon of a predctor Example: 6

More information

On a direct solver for linear least squares problems

On a direct solver for linear least squares problems ISSN 2066-6594 Ann. Acad. Rom. Sc. Ser. Math. Appl. Vol. 8, No. 2/2016 On a drect solver for lnear least squares problems Constantn Popa Abstract The Null Space (NS) algorthm s a drect solver for lnear

More information

A Tutorial on Data Reduction. Linear Discriminant Analysis (LDA) Shireen Elhabian and Aly A. Farag. University of Louisville, CVIP Lab September 2009

A Tutorial on Data Reduction. Linear Discriminant Analysis (LDA) Shireen Elhabian and Aly A. Farag. University of Louisville, CVIP Lab September 2009 A utoral on Data Reducton Lnear Dscrmnant Analss (LDA) hreen Elhaban and Al A Farag Unverst of Lousvlle, CVIP Lab eptember 009 Outlne LDA objectve Recall PCA No LDA LDA o Classes Counter eample LDA C Classes

More information

The Study of Teaching-learning-based Optimization Algorithm

The Study of Teaching-learning-based Optimization Algorithm Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute

More information

Yong Joon Ryang. 1. Introduction Consider the multicommodity transportation problem with convex quadratic cost function. 1 2 (x x0 ) T Q(x x 0 )

Yong Joon Ryang. 1. Introduction Consider the multicommodity transportation problem with convex quadratic cost function. 1 2 (x x0 ) T Q(x x 0 ) Kangweon-Kyungk Math. Jour. 4 1996), No. 1, pp. 7 16 AN ITERATIVE ROW-ACTION METHOD FOR MULTICOMMODITY TRANSPORTATION PROBLEMS Yong Joon Ryang Abstract. The optmzaton problems wth quadratc constrants often

More information

Linear Approximation with Regularization and Moving Least Squares

Linear 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 information

Dynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence)

Dynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence) /24/27 Prevew Fbonacc Sequence Longest Common Subsequence Dynamc programmng s a method for solvng complex problems by breakng them down nto smpler sub-problems. It s applcable to problems exhbtng the propertes

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Exerments-I MODULE III LECTURE - 2 EXPERIMENTAL DESIGN MODELS Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 2 We consder the models

More information

Solutions HW #2. minimize. Ax = b. Give the dual problem, and make the implicit equality constraints explicit. Solution.

Solutions 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 information

On the symmetric character of the thermal conductivity tensor

On the symmetric character of the thermal conductivity tensor On the symmetrc character of the thermal conductvty tensor Al R. Hadjesfandar Department of Mechancal and Aerospace Engneerng Unversty at Buffalo, State Unversty of New York Buffalo, NY 146 USA ah@buffalo.edu

More information

Société de Calcul Mathématique SA

Société de Calcul Mathématique SA Socété de Calcul Mathématque SA Outls d'ade à la décson Tools for decson help Probablstc Studes: Normalzng the Hstograms Bernard Beauzamy December, 202 I. General constructon of the hstogram Any probablstc

More information

A Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach

A Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach A Bayes Algorthm for the Multtask Pattern Recognton Problem Drect Approach Edward Puchala Wroclaw Unversty of Technology, Char of Systems and Computer etworks, Wybrzeze Wyspanskego 7, 50-370 Wroclaw, Poland

More information

A new Approach for Solving Linear Ordinary Differential Equations

A new Approach for Solving Linear Ordinary Differential Equations , ISSN 974-57X (Onlne), ISSN 974-5718 (Prnt), Vol. ; Issue No. 1; Year 14, Copyrght 13-14 by CESER PUBLICATIONS A new Approach for Solvng Lnear Ordnary Dfferental Equatons Fawz Abdelwahd Department of

More information

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

Lossy Compression. Compromise accuracy of reconstruction for increased compression. Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost

More information

Online Appendix. t=1 (p t w)q t. Then the first order condition shows that

Online Appendix. t=1 (p t w)q t. Then the first order condition shows that Artcle forthcomng to ; manuscrpt no (Please, provde the manuscrpt number!) 1 Onlne Appendx Appendx E: Proofs Proof of Proposton 1 Frst we derve the equlbrum when the manufacturer does not vertcally ntegrate

More information

Canonical transformations

Canonical transformations Canoncal transformatons November 23, 2014 Recall that we have defned a symplectc transformaton to be any lnear transformaton M A B leavng the symplectc form nvarant, Ω AB M A CM B DΩ CD Coordnate transformatons,

More information

An Effective Modification to Solve Transportation Problems: A Cost Minimization Approach

An Effective Modification to Solve Transportation Problems: A Cost Minimization Approach Annals of Pure and Appled Mathematcs Vol. 6, No. 2, 204, 99-206 ISSN: 2279-087X (P), 2279-0888(onlne) Publshed on 4 August 204 www.researchmathsc.org Annals of An Effectve Modfcaton to Solve Transportaton

More information

Simultaneous Optimization of Berth Allocation, Quay Crane Assignment and Quay Crane Scheduling Problems in Container Terminals

Simultaneous Optimization of Berth Allocation, Quay Crane Assignment and Quay Crane Scheduling Problems in Container Terminals Smultaneous Optmzaton of Berth Allocaton, Quay Crane Assgnment and Quay Crane Schedulng Problems n Contaner Termnals Necat Aras, Yavuz Türkoğulları, Z. Caner Taşkın, Kuban Altınel Abstract In ths work,

More information

Market structure and Innovation

Market structure and Innovation Market structure and Innovaton Ths presentaton s based on the paper Market structure and Innovaton authored by Glenn C. Loury, publshed n The Quarterly Journal of Economcs, Vol. 93, No.3 ( Aug 1979) I.

More information

Exercise Solutions to Real Analysis

Exercise Solutions to Real Analysis xercse Solutons to Real Analyss Note: References refer to H. L. Royden, Real Analyss xersze 1. Gven any set A any ɛ > 0, there s an open set O such that A O m O m A + ɛ. Soluton 1. If m A =, then there

More information

Case A. P k = Ni ( 2L i k 1 ) + (# big cells) 10d 2 P k.

Case A. P k = Ni ( 2L i k 1 ) + (# big cells) 10d 2 P k. THE CELLULAR METHOD In ths lecture, we ntroduce the cellular method as an approach to ncdence geometry theorems lke the Szemeréd-Trotter theorem. The method was ntroduced n the paper Combnatoral complexty

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Recall: man dea of lnear regresson Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 8 Lnear regresson can be used to study an

More information