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

Transcription

1 COMP331/557 Chapter 2: The Geometry of Linear Programming (Bertsimas & Tsitsiklis, Chapter 2) 49

2 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 50

3 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 b {x A x = b, x 0} is polyhedron in standard form representation 50

4 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 b {x A x = b, x 0} is polyhedron in standard form representation Definition 2.2. a Set S R n is bounded if there is K 2 R such that kxk 1 apple K for all x 2 S. 50

5 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 b {x A x = b, x 0} is polyhedron in standard form representation Definition 2.2. a Set S R n is bounded if there is K 2 R such that kxk 1 apple K for all x 2 S. b Aboundedpolyhedroniscalledpolytope. 50

6 Hyperplanes and Halfspaces Definition 2.3. Let a 2 R n \{0} and b 2 R: a set {x 2 R n a T x = b} is called hyperplane 51

7 Hyperplanes and Halfspaces Definition 2.3. Let a 2 R n \{0} and b 2 R: a set {x 2 R n a T x = b} is called hyperplane b set {x 2 R n a T x b} is called halfspace 51

8 Hyperplanes and Halfspaces Definition 2.3. Let a 2 R n \{0} and b 2 R: a set {x 2 R n a T x = b} is called hyperplane b set {x 2 R n a T x b} is called halfspace Remarks I Hyperplanes and halfspaces are convex sets. 51

9 Hyperplanes and Halfspaces Definition 2.3. Let a 2 R n \{0} and b 2 R: a set {x 2 R n a T x = b} is called hyperplane b set {x 2 R n a T x b} is called halfspace Remarks I Hyperplanes and halfspaces are convex sets. I A polyhedron is an intersection of finitely many halfspaces. 51

10 Convex Combination and Convex Hull Definition 2.4. Let x 1,...,x k 2 R n and 1,..., k 2 R 0 with k = 1. a The vector P k i=1 i x i is a convex combination of x 1,...,x k. 52

11 Convex Combination and Convex Hull Definition 2.4. Let x 1,...,x k 2 R n and 1,..., k 2 R 0 with k = 1. a The vector P k i=1 i x i is a convex combination of x 1,...,x k. b The convex hull of x 1,...,x k is the set of all convex combinations. 52

12 Convex Sets, Convex Combinations, and Convex Hulls Theorem 2.5. a The intersection of convex sets is convex. 53

13 Convex Sets, Convex Combinations, and Convex Hulls Theorem 2.5. a The intersection of convex sets is convex. b Every polyhedron is a convex set. 53

14 Convex Sets, Convex Combinations, and Convex Hulls Theorem 2.5. a The intersection of convex sets is convex. b c Every polyhedron is a convex set. Aconvexcombinationofafinitenumberofelementsofaconvexsetalsobelongs to that set. 53

15 Convex Sets, Convex Combinations, and Convex Hulls Theorem 2.5. a The intersection of convex sets is convex. b c d Every polyhedron is a convex set. Aconvexcombinationofafinitenumberofelementsofaconvexsetalsobelongs to that set. The convex hull of finitely many vectors is a convex set. 53

16 Convex Sets, Convex Combinations, and Convex Hulls Theorem 2.5. a The intersection of convex sets is convex. b c d Every polyhedron is a convex set. Aconvexcombinationofafinitenumberofelementsofaconvexsetalsobelongs to that set. The convex hull of finitely many vectors is a convex set. Corollary 2.6. The convex hull of x 1,...,x k 2 R n is the smallest (w.r.t. inclusion) convex subset of R n containing x 1,...,x k. 53

17 Extreme Points and Vertices of Polyhedra Definition 2.7. Let P R n be a polyhedron. a x 2 P is an extreme point of P if x 6= y +(1 ) z for all y, z 2 P \{x}, 0apple apple 1, i. e., x is not a convex combination of two other points in P. 54

18 Extreme Points and Vertices of Polyhedra Definition 2.7. Let P R n be a polyhedron. a x 2 P is an extreme point of P if x 6= y +(1 ) z for all y, z 2 P \{x}, 0apple apple 1, b i. e., x is not a convex combination of two other points in P. x 2 P is a vertex of P if there is some c 2 R n such that c T x < c T y for all y 2 P \{x}, i. e., x is the unique optimal solution to the LP min{c T z z 2 P}. 54