Modeling and Analysis of Hybrid Systems

Size: px
Start display at page:

Download "Modeling and Analysis of Hybrid Systems"

Transcription

1 Modeling and Analysis of Hybrid Systems 6. Convex polyhedra Prof. Dr. Erika Ábrahám Informatik 2 - LuFG Theory of Hybrid Systems RWTH Aachen University Szeged, Hungary, 27 September - 06 October 2017 Ábrahám - Hybrid Systems 1 / 18

2 Contents 1 Convex polyhedra 2 Operations on convex polyhedra Ábrahám - Hybrid Systems 2 / 18

3 (Convex) polyhedra and polytopes Ábrahám - Hybrid Systems 3 / 18

4 (Convex) polyhedra and polytopes Notation: In this lecture we use column-vector-notation, i.e., x R d is a column vector, whereas x T is a row vector. Ábrahám - Hybrid Systems 4 / 18

5 (Convex) polyhedra and polytopes Notation: In this lecture we use column-vector-notation, i.e., x R d is a column vector, whereas x T is a row vector. Denition (Polyhedron, polytope) A polyhedron in R d is Ábrahám - Hybrid Systems 4 / 18

6 (Convex) polyhedra and polytopes Notation: In this lecture we use column-vector-notation, i.e., x R d is a column vector, whereas x T is a row vector. Denition (Polyhedron, polytope) A polyhedron in R d is the solution set to a nite number n N 0 of linear inequalities a it x b i (i.e., a i1 x a in x n b i ), i = 1,..., n, with real coecients a i R d, real-valued variables x = (x 1,..., x d ) T and b i R. A bounded polyhedron is called a polytope. Ábrahám - Hybrid Systems 4 / 18

7 (Convex) polyhedra and polytopes Notation: In this lecture we use column-vector-notation, i.e., x R d is a column vector, whereas x T is a row vector. Denition (Polyhedron, polytope) A polyhedron in R d is the solution set to a nite number n N 0 of linear inequalities a it x b i (i.e., a i1 x a in x n b i ), i = 1,..., n, with real coecients a i R d, real-valued variables x = (x 1,..., x d ) T and b i R. A bounded polyhedron is called a polytope. x 2 0 x 1 Ábrahám - Hybrid Systems 4 / 18

8 (Convex) polyhedra and polytopes Notation: In this lecture we use column-vector-notation, i.e., x R d is a column vector, whereas x T is a row vector. Denition (Polyhedron, polytope) A polyhedron in R d is the solution set to a nite number n N 0 of linear inequalities a it x b i (i.e., a i1 x a in x n b i ), i = 1,..., n, with real coecients a i R d, real-valued variables x = (x 1,..., x d ) T and b i R. A bounded polyhedron is called a polytope. x 2 x 1 + 2x x 1 Ábrahám - Hybrid Systems 4 / 18

9 (Convex) polyhedra and polytopes Notation: In this lecture we use column-vector-notation, i.e., x R d is a column vector, whereas x T is a row vector. Denition (Polyhedron, polytope) A polyhedron in R d is the solution set to a nite number n N 0 of linear inequalities a it x b i (i.e., a i1 x a in x n b i ), i = 1,..., n, with real coecients a i R d, real-valued variables x = (x 1,..., x d ) T and b i R. A bounded polyhedron is called a polytope. x 2 3x 1 + 2x 2 1 x 1 + 2x x 1 Ábrahám - Hybrid Systems 4 / 18

10 (Convex) polyhedra and polytopes Notation: In this lecture we use column-vector-notation, i.e., x R d is a column vector, whereas x T is a row vector. Denition (Polyhedron, polytope) A polyhedron in R d is the solution set to a nite number n N 0 of linear inequalities a it x b i (i.e., a i1 x a in x n b i ), i = 1,..., n, with real coecients a i R d, real-valued variables x = (x 1,..., x d ) T and b i R. A bounded polyhedron is called a polytope. x 2 3x 1 + 2x 2 1 x 1 + 2x 2 12 x 1 0 x 1 7 Ábrahám - Hybrid Systems 4 / 18

11 (Convex) polyhedra and polytopes Denition (Reminder: convex sets) A set S R d is convex if Ábrahám - Hybrid Systems 5 / 18

12 (Convex) polyhedra and polytopes Denition (Reminder: convex sets) A set S R d is convex if x, y S. λ [0, 1] R. λx + (1 λ)y S. Ábrahám - Hybrid Systems 5 / 18

13 (Convex) polyhedra and polytopes Denition (Reminder: convex sets) A set S R d is convex if x, y S. λ [0, 1] R. λx + (1 λ)y S. Polyhedra are convex sets. Proof? Ábrahám - Hybrid Systems 5 / 18

14 (Convex) polyhedra and polytopes Denition (Reminder: convex sets) A set S R d is convex if x, y S. λ [0, 1] R. λx + (1 λ)y S. Polyhedra are convex sets. Proof? Depending on the form of the representation we distinguish between H-polytopes and V-polytopes Ábrahám - Hybrid Systems 5 / 18

15 H-polytopes Denition (Closed halfspace) A d-dimensional closed halfspace is a set H = {x R d a T x b} for some a R d, called the normal of the halfspace, and some b R. Ábrahám - Hybrid Systems 6 / 18

16 H-polytopes Denition (Closed halfspace) A d-dimensional closed halfspace is a set H = {x R d a T x b} for some a R d, called the normal of the halfspace, and some b R. Denition (H-polyhedron, H-polytope) A d-dimensional H-polyhedron P = n i=1 H i is the intersection of nitely many closed halfspaces. A bounded H-polyhedron is called an H-polytope. The facets of a d-dimensional H-polytope are d 1-dimensional H-polytopes. Ábrahám - Hybrid Systems 6 / 18

17 H-polytopes An H-polytope P = n H i = i=1 n i=1 {x R d a it x b i } can also be written in the form P = {x R d Ax b}. We call (A, b) the H-representation of the polytope. Ábrahám - Hybrid Systems 7 / 18

18 H-polytopes An H-polytope P = n H i = i=1 n i=1 {x R d a it x b i } can also be written in the form P = {x R d Ax b}. We call (A, b) the H-representation of the polytope. Each row of A is the normal vector to the ith facet of the polytope. Ábrahám - Hybrid Systems 7 / 18

19 H-polytopes An H-polytope P = n H i = i=1 n i=1 {x R d a it x b i } can also be written in the form P = {x R d Ax b}. We call (A, b) the H-representation of the polytope. Each row of A is the normal vector to the ith facet of the polytope. An H-polytope P has a nite number of vertices V (P ). Ábrahám - Hybrid Systems 7 / 18

20 Convex hull of a nite set of points x 2 0 x 1 Ábrahám - Hybrid Systems 8 / 18

21 Convex hull of a nite set of points x 2 0 x 1 Ábrahám - Hybrid Systems 8 / 18

22 V-polytopes Denition (Convex hull) Given a set V R d, the convex hull conv(v ) of V is the smallest convex set that contains V. Ábrahám - Hybrid Systems 9 / 18

23 V-polytopes Denition (Convex hull) Given a set V R d, the convex hull conv(v ) of V is the smallest convex set that contains V. For a nite set V = {v v 1,...,v v n } R d, its convex hull can be computed by Ábrahám - Hybrid Systems 9 / 18

24 V-polytopes Denition (Convex hull) Given a set V R d, the convex hull conv(v ) of V is the smallest convex set that contains V. For a nite set V = {v v 1,...,v v n } R d, its convex hull can be computed by conv(v ) = {x R d λ 1,..., λ n [0, 1] R. n n λ i = 1 λ i v i = x}. i=1 i=1 Ábrahám - Hybrid Systems 9 / 18

25 V-polytopes Denition (Convex hull) Given a set V R d, the convex hull conv(v ) of V is the smallest convex set that contains V. For a nite set V = {v v 1,...,v v n } R d, its convex hull can be computed by conv(v ) = {x R d λ 1,..., λ n [0, 1] R. n n λ i = 1 λ i v i = x}. i=1 i=1 Denition (V-polytope) A V-polytope P = conv(v ) is the convex hull of a nite set V R d. We call V the V-representation of the polytope. Ábrahám - Hybrid Systems 9 / 18

26 V-polytopes Denition (Convex hull) Given a set V R d, the convex hull conv(v ) of V is the smallest convex set that contains V. For a nite set V = {v v 1,...,v v n } R d, its convex hull can be computed by conv(v ) = {x R d λ 1,..., λ n [0, 1] R. n n λ i = 1 λ i v i = x}. i=1 i=1 Denition (V-polytope) A V-polytope P = conv(v ) is the convex hull of a nite set V R d. We call V the V-representation of the polytope. Note that all V-polytopes are bounded. Ábrahám - Hybrid Systems 9 / 18

27 V-polytopes Denition (Convex hull) Given a set V R d, the convex hull conv(v ) of V is the smallest convex set that contains V. For a nite set V = {v v 1,...,v v n } R d, its convex hull can be computed by conv(v ) = {x R d λ 1,..., λ n [0, 1] R. n n λ i = 1 λ i v i = x}. i=1 i=1 Denition (V-polytope) A V-polytope P = conv(v ) is the convex hull of a nite set V R d. We call V the V-representation of the polytope. Note that all V-polytopes are bounded. Unbounded polyhedra can be represented by extending convex hulls with conical hulls. Ábrahám - Hybrid Systems 9 / 18

28 Conical hull of a nite set of points x 2 0 x 1 Ábrahám - Hybrid Systems 10 / 18

29 Conical hull of a nite set of points x 2 0 x 1 Ábrahám - Hybrid Systems 10 / 18

30 Conical hull of a nite set of points Denition (Conical hull) u 1 u n If U = {uu 1,...,uu n } is a nite set of points in R d, the conical hull of U is dened by cone(u) = {x R d λ 1,..., λ n R 0. x = n i=1 λ i u i }. (1) Ábrahám - Hybrid Systems 11 / 18

31 Conical hull of a nite set of points Denition (Conical hull) u 1 u n If U = {uu 1,...,uu n } is a nite set of points in R d, the conical hull of U is dened by cone(u) = {x R d λ 1,..., λ n R 0. x = n i=1 λ i u i }. (1) Each polyhedra P R d can be represented by two nite sets V, U R d such that P = conv(v ) cone(u) = {v + u v conv(v ), u cone(u)}, where is called the Minkowski sum. If U is empty then P is bounded (e.g., a polytope). In the following we consider, for simplicity, only bounded V-polytopes. Ábrahám - Hybrid Systems 11 / 18

32 Motzkin's theorem For each H-polytope, the convex hull of its vertices denes the same set in the form of a V-polytope, and vice versa, each set dened as a V-polytope can be also given as an H-polytope by computing the halfspaces dened by its facets. The translations between the H- and the V-representations of polytopes can be exponential in the state space dimension d. Ábrahám - Hybrid Systems 12 / 18

33 Contents 1 Convex polyhedra 2 Operations on convex polyhedra Ábrahám - Hybrid Systems 13 / 18

34 Operations If we represent reachable sets of hybrid automata by polytopes, we need some operations like membership computation (x P ) linear transformation (A P ) Minkowski sum (P 1 P 2 ) intersection (P 1 P 2 ) (convex hull of the) union of two polytopes (conv(p 1 P 2 )) test for emptiness (P =?) Ábrahám - Hybrid Systems 14 / 18

35 Membership p P Membership for p R d : Ábrahám - Hybrid Systems 15 / 18

36 Membership p P Membership for p R d : H-polytope dened by Ax z: Ábrahám - Hybrid Systems 15 / 18

37 Membership p P Membership for p R d : H-polytope dened by Ax z: just substitute p for x to check if the inequation holds. Ábrahám - Hybrid Systems 15 / 18

38 Membership p P Membership for p R d : H-polytope dened by Ax z: just substitute p for x to check if the inequation holds. V-polytope dened by the vertex set V : Ábrahám - Hybrid Systems 15 / 18

39 Membership p P Membership for p R d : H-polytope dened by Ax z: just substitute p for x to check if the inequation holds. V-polytope dened by the vertex set V : check satisability of λ 1,..., λ n [0, 1] R. n λ i = 1 i=1 n i=1 λ i v i = x. Ábrahám - Hybrid Systems 15 / 18

40 Membership p P Membership for p R d : H-polytope dened by Ax z: just substitute p for x to check if the inequation holds. V-polytope dened by the vertex set V : check satisability of λ 1,..., λ n [0, 1] R. n λ i = 1 i=1 n i=1 λ i v i = x. Alternatively: Ábrahám - Hybrid Systems 15 / 18

41 Membership p P Membership for p R d : H-polytope dened by Ax z: just substitute p for x to check if the inequation holds. V-polytope dened by the vertex set V : check satisability of λ 1,..., λ n [0, 1] R. n λ i = 1 i=1 n i=1 λ i v i = x. Alternatively: convert the V-polytope into an H-polytope by computing its facets. Ábrahám - Hybrid Systems 15 / 18

42 Intersection P 1 P 2 Intersection for two polytopes P 1 and P 2 : H-polytopes dened by A 1 x b 1 and A 2 x b 2 : Ábrahám - Hybrid Systems 16 / 18

43 Intersection P 1 P 2 Intersection for two polytopes P 1 and P 2 : H-polytopes dened by A 1 x b 1 the resulting H-polytope is dened by V-polytopes dened by V 1 and V 2 : b 2 b 1 and A 2 x b 2 : ( A1 A 2 ) x ( b1 b 1 b 2 ). Ábrahám - Hybrid Systems 16 / 18

44 Intersection P 1 P 2 Intersection for two polytopes P 1 and P 2 : H-polytopes dened by A 1 x b 1 the resulting H-polytope is dened by b 2 b 1 and A 2 x b 2 : ( A1 A 2 ) x ( b1 b 1 b 2 V-polytopes dened by V 1 and V 2 : Convert P 1 and P 2 to H-polytopes and convert the result back to a V-polytope. ). Ábrahám - Hybrid Systems 16 / 18

45 Union P 1 P 2 Note that the union of two convex polytopes is in general not a convex polytope. Ábrahám - Hybrid Systems 17 / 18

46 Union P 1 P 2 Note that the union of two convex polytopes is in general not a convex polytope. take the convex hull of the union. Ábrahám - Hybrid Systems 17 / 18

47 Union P 1 P 2 Note that the union of two convex polytopes is in general not a convex polytope. take the convex hull of the union. V-polytopes dened by V 1 and V 2 : Ábrahám - Hybrid Systems 17 / 18

48 Union P 1 P 2 Note that the union of two convex polytopes is in general not a convex polytope. take the convex hull of the union. V-polytopes dened by V 1 and V 2 : V-representation V 1 V 2. H-polytopes dened by A 1 x b 1 and A 2 x b 2 : Ábrahám - Hybrid Systems 17 / 18

49 Union P 1 P 2 Note that the union of two convex polytopes is in general not a convex polytope. take the convex hull of the union. V-polytopes dened by V 1 and V 2 : V-representation V 1 V 2. H-polytopes dened by A 1 x b 1 and A 2 x b 2 : convert to V-polytopes and compute back the result. b 1 b 2 Ábrahám - Hybrid Systems 17 / 18

50 Operation complexity overview easy: doable in polynomial complexity hard: no polynomial algorithm is known x P A P P 1 P 2 P 1 P 2 P 1 P 2 V-polytope easy easy H-polytope easy hard V-polytope and V-polytope easy hard easy H-polytope and H-polytope hard easy hard V-polytope and H-polytope hard hard hard It is in general also hard to translate a V-polytope to an H-polytope or vice versa. Ábrahám - Hybrid Systems 18 / 18

Modeling and Analysis of Hybrid Systems

Modeling and Analysis of Hybrid Systems Modeling and Analysis of Hybrid Systems Convex polyhedra Prof. Dr. Erika Ábrahám Informatik 2 - LuFG Theory of Hybrid Systems RWTH Aachen University Szeged, Hungary, 27 September - 06 October 2017 Ábrahám

More information

Combinatorial Geometry & Topology arising in Game Theory and Optimization

Combinatorial Geometry & Topology arising in Game Theory and Optimization Combinatorial Geometry & Topology arising in Game Theory and Optimization Jesús A. De Loera University of California, Davis LAST EPISODE... We discuss the content of the course... Convex Sets A set is

More information

Lecture 2 - Introduction to Polytopes

Lecture 2 - Introduction to Polytopes Lecture 2 - Introduction to Polytopes Optimization and Approximation - ENS M1 Nicolas Bousquet 1 Reminder of Linear Algebra definitions Let x 1,..., x m be points in R n and λ 1,..., λ m be real numbers.

More information

ORIE 6300 Mathematical Programming I September 2, Lecture 3

ORIE 6300 Mathematical Programming I September 2, Lecture 3 ORIE 6300 Mathematical Programming I September 2, 2014 Lecturer: David P. Williamson Lecture 3 Scribe: Divya Singhvi Last time we discussed how to take dual of an LP in two different ways. Today we will

More information

DM545 Linear and Integer Programming. Lecture 2. The Simplex Method. Marco Chiarandini

DM545 Linear and Integer Programming. Lecture 2. The Simplex Method. Marco Chiarandini DM545 Linear and Integer Programming Lecture 2 The Marco Chiarandini Department of Mathematics & Computer Science University of Southern Denmark Outline 1. 2. 3. 4. Standard Form Basic Feasible Solutions

More information

maximize c, x subject to Ax b,

maximize c, x subject to Ax b, Lecture 8 Linear programming is about problems of the form maximize c, x subject to Ax b, where A R m n, x R n, c R n, and b R m, and the inequality sign means inequality in each row. The feasible set

More information

COMP331/557. Chapter 2: The Geometry of Linear Programming. (Bertsimas & Tsitsiklis, Chapter 2)

COMP331/557. Chapter 2: The Geometry of Linear Programming. (Bertsimas & Tsitsiklis, Chapter 2) COMP331/557 Chapter 2: The Geometry of Linear Programming (Bertsimas & Tsitsiklis, Chapter 2) 49 Polyhedra and Polytopes Definition 2.1. Let A 2 R m n and b 2 R m. a set {x 2 R n A x b} is called polyhedron

More information

Convex Geometry arising in Optimization

Convex Geometry arising in Optimization Convex Geometry arising in Optimization Jesús A. De Loera University of California, Davis Berlin Mathematical School Summer 2015 WHAT IS THIS COURSE ABOUT? Combinatorial Convexity and Optimization PLAN

More information

Math 414 Lecture 2 Everyone have a laptop?

Math 414 Lecture 2 Everyone have a laptop? Math 44 Lecture 2 Everyone have a laptop? THEOREM. Let v,...,v k be k vectors in an n-dimensional space and A = [v ;...; v k ] v,..., v k independent v,..., v k span the space v,..., v k a basis v,...,

More information

Polar Duality and Farkas Lemma

Polar Duality and Farkas Lemma Lecture 3 Polar Duality and Farkas Lemma October 8th, 2004 Lecturer: Kamal Jain Notes: Daniel Lowd 3.1 Polytope = bounded polyhedron Last lecture, we were attempting to prove the Minkowsky-Weyl Theorem:

More information

Linear programming and duality theory

Linear programming and duality theory Linear programming and duality theory Complements of Operations Research Giovanni Righini Linear Programming (LP) A linear program is defined by linear constraints, a linear objective function. Its variables

More information

Polyhedral Computation Today s Topic: The Double Description Algorithm. Komei Fukuda Swiss Federal Institute of Technology Zurich October 29, 2010

Polyhedral Computation Today s Topic: The Double Description Algorithm. Komei Fukuda Swiss Federal Institute of Technology Zurich October 29, 2010 Polyhedral Computation Today s Topic: The Double Description Algorithm Komei Fukuda Swiss Federal Institute of Technology Zurich October 29, 2010 1 Convexity Review: Farkas-Type Alternative Theorems Gale

More information

Linear Programming in Small Dimensions

Linear Programming in Small Dimensions Linear Programming in Small Dimensions Lekcija 7 sergio.cabello@fmf.uni-lj.si FMF Univerza v Ljubljani Edited from slides by Antoine Vigneron Outline linear programming, motivation and definition one dimensional

More information

Lecture 4: Rational IPs, Polyhedron, Decomposition Theorem

Lecture 4: Rational IPs, Polyhedron, Decomposition Theorem IE 5: Integer Programming, Spring 29 24 Jan, 29 Lecture 4: Rational IPs, Polyhedron, Decomposition Theorem Lecturer: Karthik Chandrasekaran Scribe: Setareh Taki Disclaimer: These notes have not been subjected

More information

On the Hardness of Computing Intersection, Union and Minkowski Sum of Polytopes

On the Hardness of Computing Intersection, Union and Minkowski Sum of Polytopes On the Hardness of Computing Intersection, Union and Minkowski Sum of Polytopes Hans Raj Tiwary hansraj@cs.uni-sb.de FR Informatik Universität des Saarlandes D-66123 Saarbrücken, Germany Tel: +49 681 3023235

More information

IE 5531: Engineering Optimization I

IE 5531: Engineering Optimization I IE 5531: Engineering Optimization I Lecture 3: Linear Programming, Continued Prof. John Gunnar Carlsson September 15, 2010 Prof. John Gunnar Carlsson IE 5531: Engineering Optimization I September 15, 2010

More information

Applied Integer Programming

Applied Integer Programming Applied Integer Programming D.S. Chen; R.G. Batson; Y. Dang Fahimeh 8.2 8.7 April 21, 2015 Context 8.2. Convex sets 8.3. Describing a bounded polyhedron 8.4. Describing unbounded polyhedron 8.5. Faces,

More information

POLYHEDRAL GEOMETRY. Convex functions and sets. Mathematical Programming Niels Lauritzen Recall that a subset C R n is convex if

POLYHEDRAL GEOMETRY. Convex functions and sets. Mathematical Programming Niels Lauritzen Recall that a subset C R n is convex if POLYHEDRAL GEOMETRY Mathematical Programming Niels Lauritzen 7.9.2007 Convex functions and sets Recall that a subset C R n is convex if {λx + (1 λ)y 0 λ 1} C for every x, y C and 0 λ 1. A function f :

More information

FACES OF CONVEX SETS

FACES OF CONVEX SETS FACES OF CONVEX SETS VERA ROSHCHINA Abstract. We remind the basic definitions of faces of convex sets and their basic properties. For more details see the classic references [1, 2] and [4] for polytopes.

More information

Integer Programming Theory

Integer Programming Theory Integer Programming Theory Laura Galli October 24, 2016 In the following we assume all functions are linear, hence we often drop the term linear. In discrete optimization, we seek to find a solution x

More information

Convex Optimization Lecture 2

Convex Optimization Lecture 2 Convex Optimization Lecture 2 Today: Convex Analysis Center-of-mass Algorithm 1 Convex Analysis Convex Sets Definition: A set C R n is convex if for all x, y C and all 0 λ 1, λx + (1 λ)y C Operations that

More information

Convex Hull Representation Conversion (cddlib, lrslib)

Convex Hull Representation Conversion (cddlib, lrslib) Convex Hull Representation Conversion (cddlib, lrslib) Student Seminar in Combinatorics: Mathematical Software Niklas Pfister October 31, 2014 1 Introduction In this report we try to give a short overview

More information

4 LINEAR PROGRAMMING (LP) E. Amaldi Fondamenti di R.O. Politecnico di Milano 1

4 LINEAR PROGRAMMING (LP) E. Amaldi Fondamenti di R.O. Politecnico di Milano 1 4 LINEAR PROGRAMMING (LP) E. Amaldi Fondamenti di R.O. Politecnico di Milano 1 Mathematical programming (optimization) problem: min f (x) s.t. x X R n set of feasible solutions with linear objective function

More information

Introduction to Modern Control Systems

Introduction to Modern Control Systems Introduction to Modern Control Systems Convex Optimization, Duality and Linear Matrix Inequalities Kostas Margellos University of Oxford AIMS CDT 2016-17 Introduction to Modern Control Systems November

More information

Convexity: an introduction

Convexity: an introduction Convexity: an introduction Geir Dahl CMA, Dept. of Mathematics and Dept. of Informatics University of Oslo 1 / 74 1. Introduction 1. Introduction what is convexity where does it arise main concepts and

More information

11 Linear Programming

11 Linear Programming 11 Linear Programming 11.1 Definition and Importance The final topic in this course is Linear Programming. We say that a problem is an instance of linear programming when it can be effectively expressed

More information

LP Geometry: outline. A general LP. minimize x c T x s.t. a T i. x b i, i 2 M 1 a T i x = b i, i 2 M 3 x j 0, j 2 N 1. where

LP Geometry: outline. A general LP. minimize x c T x s.t. a T i. x b i, i 2 M 1 a T i x = b i, i 2 M 3 x j 0, j 2 N 1. where LP Geometry: outline I Polyhedra I Extreme points, vertices, basic feasible solutions I Degeneracy I Existence of extreme points I Optimality of extreme points IOE 610: LP II, Fall 2013 Geometry of Linear

More information

CS522: Advanced Algorithms

CS522: Advanced Algorithms Lecture 1 CS5: Advanced Algorithms October 4, 004 Lecturer: Kamal Jain Notes: Chris Re 1.1 Plan for the week Figure 1.1: Plan for the week The underlined tools, weak duality theorem and complimentary slackness,

More information

Lecture 2 Convex Sets

Lecture 2 Convex Sets Optimization Theory and Applications Lecture 2 Convex Sets Prof. Chun-Hung Liu Dept. of Electrical and Computer Engineering National Chiao Tung University Fall 2016 2016/9/29 Lecture 2: Convex Sets 1 Outline

More information

Submodularity Reading Group. Matroid Polytopes, Polymatroid. M. Pawan Kumar

Submodularity Reading Group. Matroid Polytopes, Polymatroid. M. Pawan Kumar Submodularity Reading Group Matroid Polytopes, Polymatroid M. Pawan Kumar http://www.robots.ox.ac.uk/~oval/ Outline Linear Programming Matroid Polytopes Polymatroid Polyhedron Ax b A : m x n matrix b:

More information

CS671 Parallel Programming in the Many-Core Era

CS671 Parallel Programming in the Many-Core Era 1 CS671 Parallel Programming in the Many-Core Era Polyhedral Framework for Compilation: Polyhedral Model Representation, Data Dependence Analysis, Scheduling and Data Locality Optimizations December 3,

More information

Lecture 4: Linear Programming

Lecture 4: Linear Programming COMP36111: Advanced Algorithms I Lecture 4: Linear Programming Ian Pratt-Hartmann Room KB2.38: email: ipratt@cs.man.ac.uk 2017 18 Outline The Linear Programming Problem Geometrical analysis The Simplex

More information

Advanced Operations Research Techniques IE316. Quiz 1 Review. Dr. Ted Ralphs

Advanced Operations Research Techniques IE316. Quiz 1 Review. Dr. Ted Ralphs Advanced Operations Research Techniques IE316 Quiz 1 Review Dr. Ted Ralphs IE316 Quiz 1 Review 1 Reading for The Quiz Material covered in detail in lecture. 1.1, 1.4, 2.1-2.6, 3.1-3.3, 3.5 Background material

More information

Efficient Bounded Reachability Computation for Rectangular Automata

Efficient Bounded Reachability Computation for Rectangular Automata Efficient Bounded Reachability Computation for Rectangular Automata Xin Chen, Erika Ábrahám, and Goran Frehse RWTH Aachen University, Germany Université Grenoble Joseph Fourier - Verimag, France Abstract.

More information

arxiv: v1 [math.co] 15 Dec 2009

arxiv: v1 [math.co] 15 Dec 2009 ANOTHER PROOF OF THE FACT THAT POLYHEDRAL CONES ARE FINITELY GENERATED arxiv:092.2927v [math.co] 5 Dec 2009 VOLKER KAIBEL Abstract. In this note, we work out a simple inductive proof showing that every

More information

1 Linear Programming. 1.1 Optimizion problems and convex polytopes 1 LINEAR PROGRAMMING

1 Linear Programming. 1.1 Optimizion problems and convex polytopes 1 LINEAR PROGRAMMING 1 LINEAR PROGRAMMING 1 Linear Programming Now, we will talk a little bit about Linear Programming. We say that a problem is an instance of linear programming when it can be effectively expressed in the

More information

In this chapter we introduce some of the basic concepts that will be useful for the study of integer programming problems.

In this chapter we introduce some of the basic concepts that will be useful for the study of integer programming problems. 2 Basics In this chapter we introduce some of the basic concepts that will be useful for the study of integer programming problems. 2.1 Notation Let A R m n be a matrix with row index set M = {1,...,m}

More information

CS 473: Algorithms. Ruta Mehta. Spring University of Illinois, Urbana-Champaign. Ruta (UIUC) CS473 1 Spring / 29

CS 473: Algorithms. Ruta Mehta. Spring University of Illinois, Urbana-Champaign. Ruta (UIUC) CS473 1 Spring / 29 CS 473: Algorithms Ruta Mehta University of Illinois, Urbana-Champaign Spring 2018 Ruta (UIUC) CS473 1 Spring 2018 1 / 29 CS 473: Algorithms, Spring 2018 Simplex and LP Duality Lecture 19 March 29, 2018

More information

Discrete Optimization 2010 Lecture 5 Min-Cost Flows & Total Unimodularity

Discrete Optimization 2010 Lecture 5 Min-Cost Flows & Total Unimodularity Discrete Optimization 2010 Lecture 5 Min-Cost Flows & Total Unimodularity Marc Uetz University of Twente m.uetz@utwente.nl Lecture 5: sheet 1 / 26 Marc Uetz Discrete Optimization Outline 1 Min-Cost Flows

More information

C&O 355 Lecture 16. N. Harvey

C&O 355 Lecture 16. N. Harvey C&O 355 Lecture 16 N. Harvey Topics Review of Fourier-Motzkin Elimination Linear Transformations of Polyhedra Convex Combinations Convex Hulls Polytopes & Convex Hulls Fourier-Motzkin Elimination Joseph

More information

CS 372: Computational Geometry Lecture 10 Linear Programming in Fixed Dimension

CS 372: Computational Geometry Lecture 10 Linear Programming in Fixed Dimension CS 372: Computational Geometry Lecture 10 Linear Programming in Fixed Dimension Antoine Vigneron King Abdullah University of Science and Technology November 7, 2012 Antoine Vigneron (KAUST) CS 372 Lecture

More information

Lecture Notes 2: The Simplex Algorithm

Lecture Notes 2: The Simplex Algorithm Algorithmic Methods 25/10/2010 Lecture Notes 2: The Simplex Algorithm Professor: Yossi Azar Scribe:Kiril Solovey 1 Introduction In this lecture we will present the Simplex algorithm, finish some unresolved

More information

Simplex Algorithm in 1 Slide

Simplex Algorithm in 1 Slide Administrivia 1 Canonical form: Simplex Algorithm in 1 Slide If we do pivot in A r,s >0, where c s

More information

Lecture 5: Properties of convex sets

Lecture 5: Properties of convex sets Lecture 5: Properties of convex sets Rajat Mittal IIT Kanpur This week we will see properties of convex sets. These properties make convex sets special and are the reason why convex optimization problems

More information

EC 521 MATHEMATICAL METHODS FOR ECONOMICS. Lecture 2: Convex Sets

EC 521 MATHEMATICAL METHODS FOR ECONOMICS. Lecture 2: Convex Sets EC 51 MATHEMATICAL METHODS FOR ECONOMICS Lecture : Convex Sets Murat YILMAZ Boğaziçi University In this section, we focus on convex sets, separating hyperplane theorems and Farkas Lemma. And as an application

More information

Math 5593 Linear Programming Lecture Notes

Math 5593 Linear Programming Lecture Notes Math 5593 Linear Programming Lecture Notes Unit II: Theory & Foundations (Convex Analysis) University of Colorado Denver, Fall 2013 Topics 1 Convex Sets 1 1.1 Basic Properties (Luenberger-Ye Appendix B.1).........................

More information

Combinatorial Optimization and Integer Linear Programming

Combinatorial Optimization and Integer Linear Programming Combinatorial Optimization and Integer Linear Programming 3 Combinatorial Optimization: Introduction Many problems arising in practical applications have a special, discrete and finite, nature: Definition.

More information

Polytopes Course Notes

Polytopes Course Notes Polytopes Course Notes Carl W. Lee Department of Mathematics University of Kentucky Lexington, KY 40506 lee@ms.uky.edu Fall 2013 i Contents 1 Polytopes 1 1.1 Convex Combinations and V-Polytopes.....................

More information

Simplicial Cells in Arrangements of Hyperplanes

Simplicial Cells in Arrangements of Hyperplanes Simplicial Cells in Arrangements of Hyperplanes Christoph Dätwyler 05.01.2013 This paper is a report written due to the authors presentation of a paper written by Shannon [1] in 1977. The presentation

More information

AMS : Combinatorial Optimization Homework Problems - Week V

AMS : Combinatorial Optimization Homework Problems - Week V AMS 553.766: Combinatorial Optimization Homework Problems - Week V For the following problems, A R m n will be m n matrices, and b R m. An affine subspace is the set of solutions to a a system of linear

More information

MA4254: Discrete Optimization. Defeng Sun. Department of Mathematics National University of Singapore Office: S Telephone:

MA4254: Discrete Optimization. Defeng Sun. Department of Mathematics National University of Singapore Office: S Telephone: MA4254: Discrete Optimization Defeng Sun Department of Mathematics National University of Singapore Office: S14-04-25 Telephone: 6516 3343 Aims/Objectives: Discrete optimization deals with problems of

More information

CMU-Q Lecture 9: Optimization II: Constrained,Unconstrained Optimization Convex optimization. Teacher: Gianni A. Di Caro

CMU-Q Lecture 9: Optimization II: Constrained,Unconstrained Optimization Convex optimization. Teacher: Gianni A. Di Caro CMU-Q 15-381 Lecture 9: Optimization II: Constrained,Unconstrained Optimization Convex optimization Teacher: Gianni A. Di Caro GLOBAL FUNCTION OPTIMIZATION Find the global maximum of the function f x (and

More information

Mathematical Programming and Research Methods (Part II)

Mathematical Programming and Research Methods (Part II) Mathematical Programming and Research Methods (Part II) 4. Convexity and Optimization Massimiliano Pontil (based on previous lecture by Andreas Argyriou) 1 Today s Plan Convex sets and functions Types

More information

Decision Aid Methodologies In Transportation Lecture 1: Polyhedra and Simplex method

Decision Aid Methodologies In Transportation Lecture 1: Polyhedra and Simplex method Decision Aid Methodologies In Transportation Lecture 1: Polyhedra and Simplex method Chen Jiang Hang Transportation and Mobility Laboratory April 15, 2013 Chen Jiang Hang (Transportation and Mobility Decision

More information

Figure 2.1: An example of a convex set and a nonconvex one.

Figure 2.1: An example of a convex set and a nonconvex one. Convex Hulls 2.1 Definitions 2 Convexity is the key to understanding and simplifying geometry, and the convex hull plays a role in geometry akin to the sorted order for a collection of numbers. So what

More information

R n a T i x = b i} is a Hyperplane.

R n a T i x = b i} is a Hyperplane. Geometry of LPs Consider the following LP : min {c T x a T i x b i The feasible region is i =1,...,m}. X := {x R n a T i x b i i =1,...,m} = m i=1 {x Rn a T i x b i} }{{} X i The set X i is a Half-space.

More information

Advanced Linear Programming. Organisation. Lecturers: Leen Stougie, CWI and Vrije Universiteit in Amsterdam

Advanced Linear Programming. Organisation. Lecturers: Leen Stougie, CWI and Vrije Universiteit in Amsterdam Advanced Linear Programming Organisation Lecturers: Leen Stougie, CWI and Vrije Universiteit in Amsterdam E-mail: stougie@cwi.nl Marjan van den Akker Universiteit Utrecht marjan@cs.uu.nl Advanced Linear

More information

Planar Graphs. 1 Graphs and maps. 1.1 Planarity and duality

Planar Graphs. 1 Graphs and maps. 1.1 Planarity and duality Planar Graphs In the first half of this book, we consider mostly planar graphs and their geometric representations, mostly in the plane. We start with a survey of basic results on planar graphs. This chapter

More information

Section Notes 5. Review of Linear Programming. Applied Math / Engineering Sciences 121. Week of October 15, 2017

Section Notes 5. Review of Linear Programming. Applied Math / Engineering Sciences 121. Week of October 15, 2017 Section Notes 5 Review of Linear Programming Applied Math / Engineering Sciences 121 Week of October 15, 2017 The following list of topics is an overview of the material that was covered in the lectures

More information

CS 473: Algorithms. Ruta Mehta. Spring University of Illinois, Urbana-Champaign. Ruta (UIUC) CS473 1 Spring / 50

CS 473: Algorithms. Ruta Mehta. Spring University of Illinois, Urbana-Champaign. Ruta (UIUC) CS473 1 Spring / 50 CS 473: Algorithms Ruta Mehta University of Illinois, Urbana-Champaign Spring 2018 Ruta (UIUC) CS473 1 Spring 2018 1 / 50 CS 473: Algorithms, Spring 2018 Introduction to Linear Programming Lecture 18 March

More information

Linear Programming and its Applications

Linear Programming and its Applications Linear Programming and its Applications Outline for Today What is linear programming (LP)? Examples Formal definition Geometric intuition Why is LP useful? A first look at LP algorithms Duality Linear

More information

College of Computer & Information Science Fall 2007 Northeastern University 14 September 2007

College of Computer & Information Science Fall 2007 Northeastern University 14 September 2007 College of Computer & Information Science Fall 2007 Northeastern University 14 September 2007 CS G399: Algorithmic Power Tools I Scribe: Eric Robinson Lecture Outline: Linear Programming: Vertex Definitions

More information

Lecture 6: Faces, Facets

Lecture 6: Faces, Facets IE 511: Integer Programming, Spring 2019 31 Jan, 2019 Lecturer: Karthik Chandrasekaran Lecture 6: Faces, Facets Scribe: Setareh Taki Disclaimer: These notes have not been subjected to the usual scrutiny

More information

Numerical Optimization

Numerical Optimization Convex Sets Computer Science and Automation Indian Institute of Science Bangalore 560 012, India. NPTEL Course on Let x 1, x 2 R n, x 1 x 2. Line and line segment Line passing through x 1 and x 2 : {y

More information

arxiv: v1 [cs.cg] 20 Jun 2008

arxiv: v1 [cs.cg] 20 Jun 2008 On Computing the Vertex Centroid of a Polyhedron Khaled Elbassioni elbassio@mpi-sb.mpg.de Hans Raj Tiwary hansraj@cs.uni-sb.de arxiv:86.3456v [cs.cg] Jun 8 Abstract Let P be an H-polytope in R d with vertex

More information

2. Convex sets. x 1. x 2. affine set: contains the line through any two distinct points in the set

2. Convex sets. x 1. x 2. affine set: contains the line through any two distinct points in the set 2. Convex sets Convex Optimization Boyd & Vandenberghe affine and convex sets some important examples operations that preserve convexity generalized inequalities separating and supporting hyperplanes dual

More information

Semistandard Young Tableaux Polytopes. Sara Solhjem Joint work with Jessica Striker. April 9, 2017

Semistandard Young Tableaux Polytopes. Sara Solhjem Joint work with Jessica Striker. April 9, 2017 Semistandard Young Tableaux Polytopes Sara Solhjem Joint work with Jessica Striker North Dakota State University Graduate Student Combinatorics Conference 217 April 9, 217 Sara Solhjem (NDSU) Semistandard

More information

Convex Optimization. Convex Sets. ENSAE: Optimisation 1/24

Convex Optimization. Convex Sets. ENSAE: Optimisation 1/24 Convex Optimization Convex Sets ENSAE: Optimisation 1/24 Today affine and convex sets some important examples operations that preserve convexity generalized inequalities separating and supporting hyperplanes

More information

Lecture notes on the simplex method September We will present an algorithm to solve linear programs of the form. maximize.

Lecture notes on the simplex method September We will present an algorithm to solve linear programs of the form. maximize. Cornell University, Fall 2017 CS 6820: Algorithms Lecture notes on the simplex method September 2017 1 The Simplex Method We will present an algorithm to solve linear programs of the form maximize subject

More information

Parallelohedra and the Voronoi Conjecture

Parallelohedra and the Voronoi Conjecture Alexey Garber Bielefeld University November 28, 2012 Parallelohedra Denition Convex d-dimensional polytope P is called a parallelohedron if R d can be (face-to-face) tiled into parallel copies of P. Parallelohedra

More information

5.3 Cutting plane methods and Gomory fractional cuts

5.3 Cutting plane methods and Gomory fractional cuts 5.3 Cutting plane methods and Gomory fractional cuts (ILP) min c T x s.t. Ax b x 0integer feasible region X Assumption: a ij, c j and b i integer. Observation: The feasible region of an ILP can be described

More information

Convex Sets. CSCI5254: Convex Optimization & Its Applications. subspaces, affine sets, and convex sets. operations that preserve convexity

Convex Sets. CSCI5254: Convex Optimization & Its Applications. subspaces, affine sets, and convex sets. operations that preserve convexity CSCI5254: Convex Optimization & Its Applications Convex Sets subspaces, affine sets, and convex sets operations that preserve convexity generalized inequalities separating and supporting hyperplanes dual

More information

Lesson 17. Geometry and Algebra of Corner Points

Lesson 17. Geometry and Algebra of Corner Points SA305 Linear Programming Spring 2016 Asst. Prof. Nelson Uhan 0 Warm up Lesson 17. Geometry and Algebra of Corner Points Example 1. Consider the system of equations 3 + 7x 3 = 17 + 5 = 1 2 + 11x 3 = 24

More information

3. The Simplex algorithmn The Simplex algorithmn 3.1 Forms of linear programs

3. The Simplex algorithmn The Simplex algorithmn 3.1 Forms of linear programs 11 3.1 Forms of linear programs... 12 3.2 Basic feasible solutions... 13 3.3 The geometry of linear programs... 14 3.4 Local search among basic feasible solutions... 15 3.5 Organization in tableaus...

More information

On Unbounded Tolerable Solution Sets

On Unbounded Tolerable Solution Sets Reliable Computing (2005) 11: 425 432 DOI: 10.1007/s11155-005-0049-9 c Springer 2005 On Unbounded Tolerable Solution Sets IRENE A. SHARAYA Institute of Computational Technologies, 6, Acad. Lavrentiev av.,

More information

2. Convex sets. affine and convex sets. some important examples. operations that preserve convexity. generalized inequalities

2. Convex sets. affine and convex sets. some important examples. operations that preserve convexity. generalized inequalities 2. Convex sets Convex Optimization Boyd & Vandenberghe affine and convex sets some important examples operations that preserve convexity generalized inequalities separating and supporting hyperplanes dual

More information

A mini-introduction to convexity

A mini-introduction to convexity A mini-introduction to convexity Geir Dahl March 14, 2017 1 Introduction Convexity, or convex analysis, is an area of mathematics where one studies questions related to two basic objects, namely convex

More information

MATH 890 HOMEWORK 2 DAVID MEREDITH

MATH 890 HOMEWORK 2 DAVID MEREDITH MATH 890 HOMEWORK 2 DAVID MEREDITH (1) Suppose P and Q are polyhedra. Then P Q is a polyhedron. Moreover if P and Q are polytopes then P Q is a polytope. The facets of P Q are either F Q where F is a facet

More information

Techniques and Tools for Hybrid Systems Reachability Analysis

Techniques and Tools for Hybrid Systems Reachability Analysis which is funded by the German Research Council (DFG). Techniques and Tools for Hybrid Systems Reachability Analysis Stefan Schupp Johanna Nellen Erika Ábrahám RiSE4CPS, Heidelberg, Germany April 23, 2017

More information

Introduction to optimization

Introduction to optimization Introduction to optimization G. Ferrari Trecate Dipartimento di Ingegneria Industriale e dell Informazione Università degli Studi di Pavia Industrial Automation Ferrari Trecate (DIS) Optimization Industrial

More information

Using Facets of a MILP Model for solving a Job Shop Scheduling Problem

Using Facets of a MILP Model for solving a Job Shop Scheduling Problem Using Facets of a MILP Model for solving a Job Shop Scheduling Problem Master s Thesis in Complex Adaptive Systems VIKTOR FORSMAN Department of Mathematical Sciences Gothenburg University Gothenburg, Sweden

More information

6.854 Advanced Algorithms. Scribes: Jay Kumar Sundararajan. Duality

6.854 Advanced Algorithms. Scribes: Jay Kumar Sundararajan. Duality 6.854 Advanced Algorithms Scribes: Jay Kumar Sundararajan Lecturer: David Karger Duality This lecture covers weak and strong duality, and also explains the rules for finding the dual of a linear program,

More information

EE/ACM Applications of Convex Optimization in Signal Processing and Communications Lecture 6

EE/ACM Applications of Convex Optimization in Signal Processing and Communications Lecture 6 EE/ACM 150 - Applications of Convex Optimization in Signal Processing and Communications Lecture 6 Andre Tkacenko Signal Processing Research Group Jet Propulsion Laboratory April 19, 2012 Andre Tkacenko

More information

Lecture 3: Convex sets

Lecture 3: Convex sets Lecture 3: Convex sets Rajat Mittal IIT Kanpur We denote the set of real numbers as R. Most of the time we will be working with space R n and its elements will be called vectors. Remember that a subspace

More information

Optimality certificates for convex minimization and Helly numbers

Optimality certificates for convex minimization and Helly numbers Optimality certificates for convex minimization and Helly numbers Amitabh Basu Michele Conforti Gérard Cornuéjols Robert Weismantel Stefan Weltge October 20, 2016 Abstract We consider the problem of minimizing

More information

Lecture 2: August 31

Lecture 2: August 31 10-725/36-725: Convex Optimization Fall 2016 Lecture 2: August 31 Lecturer: Lecturer: Ryan Tibshirani Scribes: Scribes: Lidan Mu, Simon Du, Binxuan Huang 2.1 Review A convex optimization problem is of

More information

Lecture 12 March 4th

Lecture 12 March 4th Math 239: Discrete Mathematics for the Life Sciences Spring 2008 Lecture 12 March 4th Lecturer: Lior Pachter Scribe/ Editor: Wenjing Zheng/ Shaowei Lin 12.1 Alignment Polytopes Recall that the alignment

More information

Chapter 4 Concepts from Geometry

Chapter 4 Concepts from Geometry Chapter 4 Concepts from Geometry An Introduction to Optimization Spring, 2014 Wei-Ta Chu 1 Line Segments The line segment between two points and in R n is the set of points on the straight line joining

More information

Linear Programming. Readings: Read text section 11.6, and sections 1 and 2 of Tom Ferguson s notes (see course homepage).

Linear Programming. Readings: Read text section 11.6, and sections 1 and 2 of Tom Ferguson s notes (see course homepage). Linear Programming Learning Goals. Introduce Linear Programming Problems. Widget Example, Graphical Solution. Basic Theory: Feasible Set, Vertices, Existence of Solutions. Equivalent formulations. Outline

More information

Hausdorff Approximation of 3D Convex Polytopes

Hausdorff Approximation of 3D Convex Polytopes Hausdorff Approximation of 3D Convex Polytopes Mario A. Lopez Department of Mathematics University of Denver Denver, CO 80208, U.S.A. mlopez@cs.du.edu Shlomo Reisner Department of Mathematics University

More information

Linear Programming. Widget Factory Example. Linear Programming: Standard Form. Widget Factory Example: Continued.

Linear Programming. Widget Factory Example. Linear Programming: Standard Form. Widget Factory Example: Continued. Linear Programming Widget Factory Example Learning Goals. Introduce Linear Programming Problems. Widget Example, Graphical Solution. Basic Theory:, Vertices, Existence of Solutions. Equivalent formulations.

More information

Convex hulls of spheres and convex hulls of convex polytopes lying on parallel hyperplanes

Convex hulls of spheres and convex hulls of convex polytopes lying on parallel hyperplanes Convex hulls of spheres and convex hulls of convex polytopes lying on parallel hyperplanes Menelaos I. Karavelas joint work with Eleni Tzanaki University of Crete & FO.R.T.H. OrbiCG/ Workshop on Computational

More information

Belt diameter of some class of space lling zonotopes

Belt diameter of some class of space lling zonotopes Belt diameter of some class of space lling zonotopes Alexey Garber Moscow State University and Delone Laboratory of Yaroslavl State University, Russia Alexandro Readings, Moscow State University May 22,

More information

CS675: Convex and Combinatorial Optimization Spring 2018 The Simplex Algorithm. Instructor: Shaddin Dughmi

CS675: Convex and Combinatorial Optimization Spring 2018 The Simplex Algorithm. Instructor: Shaddin Dughmi CS675: Convex and Combinatorial Optimization Spring 2018 The Simplex Algorithm Instructor: Shaddin Dughmi Algorithms for Convex Optimization We will look at 2 algorithms in detail: Simplex and Ellipsoid.

More information

Week 5. Convex Optimization

Week 5. Convex Optimization Week 5. Convex Optimization Lecturer: Prof. Santosh Vempala Scribe: Xin Wang, Zihao Li Feb. 9 and, 206 Week 5. Convex Optimization. The convex optimization formulation A general optimization problem is

More information

Optimality certificates for convex minimization and Helly numbers

Optimality certificates for convex minimization and Helly numbers Optimality certificates for convex minimization and Helly numbers Amitabh Basu Michele Conforti Gérard Cornuéjols Robert Weismantel Stefan Weltge May 10, 2017 Abstract We consider the problem of minimizing

More information

CS675: Convex and Combinatorial Optimization Spring 2018 Convex Sets. Instructor: Shaddin Dughmi

CS675: Convex and Combinatorial Optimization Spring 2018 Convex Sets. Instructor: Shaddin Dughmi CS675: Convex and Combinatorial Optimization Spring 2018 Convex Sets Instructor: Shaddin Dughmi Outline 1 Convex sets, Affine sets, and Cones 2 Examples of Convex Sets 3 Convexity-Preserving Operations

More information

Chapter 8. Voronoi Diagrams. 8.1 Post Oce Problem

Chapter 8. Voronoi Diagrams. 8.1 Post Oce Problem Chapter 8 Voronoi Diagrams 8.1 Post Oce Problem Suppose there are n post oces p 1,... p n in a city. Someone who is located at a position q within the city would like to know which post oce is closest

More information

Lecture 1: Introduction

Lecture 1: Introduction Lecture 1 1 Linear and Combinatorial Optimization Anders Heyden Centre for Mathematical Sciences Lecture 1: Introduction The course and its goals Basic concepts Optimization Combinatorial optimization

More information

We have set up our axioms to deal with the geometry of space but have not yet developed these ideas much. Let s redress that imbalance.

We have set up our axioms to deal with the geometry of space but have not yet developed these ideas much. Let s redress that imbalance. Solid geometry We have set up our axioms to deal with the geometry of space but have not yet developed these ideas much. Let s redress that imbalance. First, note that everything we have proven for the

More information