Convex Geometry arising in Optimization

Transcription

1 Convex Geometry arising in Optimization Jesús A. De Loera University of California, Davis Berlin Mathematical School Summer 2015

2 WHAT IS THIS COURSE ABOUT?

3 Combinatorial Convexity and Optimization PLAN 6 lectures on convex geometry, with emphasis around the classical theorems of Combinatorial Convexity and their impact in Linear and Integer Optimization. ASSUMPTION: You have solid knowledge of Linear Algebra, introductory Analysis and a very strong liking of Combinatorics and Optimization. Everything we do takes place inside Euclidean d-dimensional space R d, with the traditional Euclidean inner-product, norm of vectors, and distance between two points and its linear structure. SO we begin... Once upon a time there was a very pretty, and brave set in R d,...

4 Convex Sets A set is CONVEX if it contains any line segment joining two of its points: NOT CONVEX CONVEX The line segment between x and y is given by [x, y] := {αx + (1 α)y : 0 α 1} EXERCISE Prove or disprove: the image of a convex set under a linear transformation is again a convex set.

5 Examples

6 HYPERPLANES A linear functional f : R d R is given by a vector c R d, c 0. For a number α R we say that H α = {x R d : f (x) = α} is an affine hyperplane or hyperplane for short. The intersection of finitely many hyperplanes is an affine space. The affine hull of a set A is the smallest affine space containing A. Affine spaces are important examples of convex sets in particular because they allow us to speak about dimension: The dimension of an affine set is the largest number of affinely independent points in the set minus one. The dimension of a convex set in R d is the dimension of its affine hull.

7 HALF-SPACES A hyperplane divides R d into two halfspaces H + α = {x R d : f (x) α} and H α = {x R d : f (x) α}. Half-spaces are convex sets each denoted formally by a linear inequality: a 1 x 1 + a 2 x a d x d b

8 For a convex set S in R d. A linear inequality f (x) α is said to be valid on S if every point in S satisfies it. A set F S is a face of S if there exists a linear inequality f (x) α which is valid on P and such that F = {x P : f (x) = α}. The hyperplane defined by f is a supporting hyperplane of F. It defines a supporting half-space A face of dimension 0 is called a vertex. A face of dimension 1 is called an edge, and a face of dimension dim(p) 1 is called a facet.

9 Let K be a closed and bounded convex set in R d. Let x 0 / K. Then, There is a unique nearest point x1 of K to x 0. The hyperplane H through x1 orthogonal to x 1 x 0 is a supporting hyperplane of K. A hyperplane H red separates sets X and Y if and only if X and Y lie in different closed halfspaces of H. If X and Y lie in different open halfspaces, we say that H strictly separates X and Y.

10 CONVEX BODIES ARE INTERSECTION OF HALF-SPACES!!! Theorem A convex body K is the intersection of its closed supporting half-spaces. Theorem convex bodies are the sets of solutions of systems of LINEAR inequalities. WARNING: It may require infinitely many hyperplanes Proposition: The intersection of convex sets is always convex.

11 POLYHEDRA: THE INTERSECTION OF FINITELY MANY HALF-SPACES

12 SOLVABILITY OF SYSTEMS OF LINEAR INEQUALITIES Find a vector (x 1, x 2,..., x d ), satisfying: a 1,1 x 1 + a 1,2 x a 1,d x d b 1 a 2,1 x 1 + a 2,2 x a 2,d x d b 2. a k,1 x 1 + a k,2 x a k,d x d b k This is the Linear feasibility problem

13 Convex Sets are EVERYWHERE!

14 and ALTHOUGH not all sets in nature are convex!

15 Convex Sets APPROXIMATE ALL SHAPES! Let A R d. The convex hull of A, denoted by conv(a), is the intersection of all the convex sets containing A. The smallest convex set that contains A. A polytope is the convex hull of a finite set of points in R d. It is the smallest convex set containing the points.

16 linear convex and conic combinations Definition: Given finitely many points A := {x 1, x 2,..., x n } we say the linear combination γ i x i is a conic combination is one P with all γi non-negative. an affine combination if γi = 1. a convex combination if it is affine and γi 0 for all i. Lemma: (EXERCISE) For a set of points A in R d we have that conv(a) equals all finite convex combinations of A: conv(a) = { x i A γ i x i : γ i 0 and γ γ k = 1} Definition A set of points x 1,..., x n is affinely dependent if there is a linear combination a i x i = 0 with a i = 0. Otherwise we say they are affinely independent. Lemma: A set of d + 2 or more points in R d is affinely dependent. Lemma: A set B R d is affinely independent every point has a unique representation as an affine combination of points in B. A set C R d is a convex cone if it is convex and for each x C, the ray 0x is fully contained in C.

17 Weyl-Minkowski: How to represent the points of a polyhedron? There are TWO ways to represent a convex set: As the intersection of half-spaces OR as the convex/conic hull of extreme points. For polyhedra, even better!! Either as a finite system of inequalities or with finitely many generators.

18 Weyl-Minkowski Theorem Theorem: (Weyl-Minkowski s Theorem): For a polyhedral subset P of R d the following statements are equivalent: P is an H-polyhedron, i.e., P is given by a system of linear inequalities P = {x : Ax b}. P is a V-polyhedron, i.e., For finitely many vectors v1,..., v n and r 1,..., r s we can write P = conv(v 1, v 2,..., v n) + cone(r 1, r 2,..., r s) R + S denotes the Minkowski sum of two sets, R + S = {r + s : r R, s S}. There are algorithms for the conversion between the H-polyhedron and V-polyhedron. NOTE: Any cone can be decomposed into a pointed cone plus a linear space.

19 EXERCISE Prove the separation theorem: Let C, D R d be convex sets with C D =. Then there exists a hyperplane h such that C lies in one of the closed half-spaces determined by h, and D lies in the opposite closed half-space. I.e., there exist a unit vector a R d and a number b R such that for all x C we have a, x b, and for all x D we have a, x b. If C and D are closed and at least one of them is bounded, they can be separated strictly; in such a way that C h = D h =. EXERCISE Let C be a cone in R d and b C a point. Prove that there exists a vector a with a, x 0 for all x C and a, b < 0. EXERCISE Let X R d. Prove that diam(conv(x)) = diam(x).

20 The 3 Jewels of Combinatorial Convexity Here are three classical theorems about convex sets. We invite you to provide proofs for them (EXERCISE)!! Theorem: (Caratheodory s theorem): If x conv(s) R d, then x is the convex combination of d + 1 points. Theorem: (Helly s theorem): If C is a finite collection of convex sets in R d such that each d + 1 sets have nonempty intersection then the intersection of all sets in C is non-empty. Theorem: (Radon s theorem): If a set A with d + 2 points in R d then A can be partitioned into two sets X, Y such that conv(x) conv(y ).