Linear Programming. Larry Blume. Cornell University & The Santa Fe Institute & IHS

Transcription

1 Linear Programming Larry Blume Cornell University & The Santa Fe Institute & IHS

2 Linear Programs The general linear program is a constrained optimization problem where objectives and constraints are all described by linear functions: v P (b) = max a x s.t. Ax b x 0 where a,x R n, and the matrix A is m n. This is the canonical form of the primal problem. The function v P (b) is the value function for the problem. How can one have inequalities in the other direction, or equality constraints? (P) Linear Programming 1

3 Linear Programs The standard form of a linear program is v P (b ) = max a x s.t. A x = b x 0 (P ) Go from the canonical to the standard form by adding slack variables z: v P (b) = max a x s.t. Ax +Iz = b x 0 z 0 where A is m n and I is the m m identity matrix. The matrix [A I] is the augmented matrix for the canonical form P. (P ) Linear Programming 2

4 Linear Programs Check that x is feasible for P if and only if there is a z such that (x,z) is feasible for P. Represent problems with inequality constraints in both directions in the canonical and standard forms. Represent minimization problems in both forms. Represent problems with equality constraints in both forms. Linear Programming 3

5 Definitions The objective function is f(x) = a x. The constraint set is C = {x : Ax b &x 0}, a convex polyhedron. A solution is a vector x R n. A feasible solution is an element of C. An optimal solution is a feasible solution which maximizes the objective function on the set C. Give examples of linear programs with a) no feasible solutions, and b) feasible solutions, but no optimal solutions. Linear Programming 4

6 The Geometry of Linear Programming Let C = {x : Ax = b} (standard form) Definition: x is a vertex of the polyhedron C iff there is no y 0 such that x +y and x y are both in P. Vertex Theorem: i) A vertex exists. ii) If v P (b) < and x C, then there is a vertex x C such that a x a x. The proof shows that C has a vertex. Not all forms of programs in have a vertex (give an example), and this is why we convert to the standard form. Linear Programming 5

7 Fundamental Theorem of Linear Programming Definition: A solution to an linear programming problem in standard form is a basic solution if and only if the set of all columns A j of A such that x j > 0 is linearly independent; that is, if the submatrix A x of A consisting of the columns A j has full column rank. Theorem: A solution x is basic if and only if x is a vertex. Fundamental Theorem: If (P) has a feasible solution, then it has a basic feasible solution. If (P) has an optimal solution, then it has a basic optimal solution. Proof: The vertex theorem implies that if a feasible solution exists, a vertex exists. There can be only a finite number of basic solutions, so (P) has only a finite number of vertices. The vertex theorem implies that the sup of the objective on C is the sup of the objective on the set of vertices, so if the sup is finite, it is realized at a vertex. Linear Programming 6

8 Duality The dual program for problem (P) is v D (a) = min y b s.t. ya a y 0 (D) Linear Programming 7

9 Duality What is the relationship between (P) and (D)? Write down the Lagrangean for each. For problem (P), letting y denote the multipliers, L(x,y) = a x +y b yax. For problem (D) letting x denote the multipliers, L(y,x) = b y xay +x a To find a saddle-point of the Lagrangean L(x,y) is to solve both the primal and the dual problems. Linear Programming 8

10 Duality Theorem Theorem: i) If either problem (P) or problem (D) has a finite optimal value, then both have an optimal solution. ii) If x and y are feasible for the primal and dual, then they are solutions if and only if a x = y b. iii) One problem has an infeasible solution if and only if the other problem is unbounded. a x yax and yax y b, so for all feasible primal solutions x and dual solutions y, a x y b. This proves iii). If x and y are feasible for (P) and (D), respectively, and a x = y b, then each expression bounds the value of the solution for the other problem If equality holds, then both bounds are achieved, and hence these solutions are optimal. This is one direction of ii). The usual proofs make use of the simplex method, which is not worth introducing. I will provide a proof from general convex duality later. Linear Programming 9

11 Complementary Slackness The primal and dual problems are: max a x s.t. Ax b x 0 min y b s.t. ya a y 0 Complementary Slackness Theorem: Suppose that x and y are feasible for the primal and dual problems, respectively. Then x and y are optimal for their respective problems if and only if y (b Ax ) = 0 = (y A a)x. Linear Programming 10

12 Complementary Slackness Interpretation y (b Ax ) = 0 = (y A a)x. The four vectors y, b Ax, y A b and x are non-negative. So in the vector inner-products, at least one of each i th coefficient must be 0. If a constraint in the primal is not binding, then the corresponding dual variable is 0. If a constraint in the dual is not binding, then the corresponding primal variable is 0. This hints at sensitivity analysis if a constraint is not binding, there is no gain to relaxing it or loss to tightening it. Linear Programming 11

13 Proof of the Complementary Slackness Theorem Suppose that x and y are feasible solutions that satisfy the complementary slackness conditions. Then y b = ax, and optimality follows from the duality theorem. If x and y are optimal, then since Ax b and y is non-negative, y Ax y b. Similarly ax y Ax. The duality theorem has y b = ax, so y Ax = y b and ax = y Ax. Linear Programming 12

14 Sensitivity Analysis Concave Functions Mathematicians write in terms of convex functions and minimization. We are interested in concave functions and maximization. These notes are for economists. We all know what concave functions are, but it is convenient to have an alternative description. Suppose that S is a subset of R n. Let Re = R {, }. Definition: The subgraph of a function f : S Re is subf = {(x,µ) S R : µ f(x)}. The function f is concave if subf is convex in R n+1. The effective domain of f is domf = {x : µ s.t. (x,µ) subf} = {x : f(x) > }. A weak continuity requirement of a function f is that its subgraph is closed. Continuous functions have closed subgraphs. Is the converse true? Exercise: Show that v P (b) has a closed subgraph. Linear Programming 13

15 Sensitivity Analysis Derivatives If concave f is smooth, f(z) f(x)+f (x)(z x) for all x and z in the domain. That is, f lies below any tangent to its subgraph. If f is not smooth, we can still support subf at any point (x,f(x)) R n+1. Definition: A supergradient to f at x is a vector x such that for all z domf, f(z) f(x)+x (z x). Definition: The superdifferential f : domf R n is the correspondence that maps each x domf to the set of all the supergradients of f at x. Linear Programming 14

16 Sensitivity Analysis An Example Suppose f(x) = { 2x if x 0, x if x 0. The supergradient of f is {2} if x < 0, f(x) = [1,2] if x = 0, {1} if x > 0. Linear Programming 15

17 Sensitivity Analysis Shadow Prices Theorem: The value function v P (b) is concave, and if y solves (D) with objective b, then y v P (b). Proof: Suppose x and x solve (P) with constraints b and b. For λ [0,1], λx +(1 λ)x is feasible for λb +(1 λ)b, so v P ( λb +(1 λ)b ) a(λx +(1 λ)x ) = λv P (b )+(1 λ)v P (b ). If y solves the dual with objective vector b, then y 0 and y A a. Choose another objective vector b with optimal solution x for (P) and y for (D). Then y is feasible for this dual problem too. Then y b y b = ax = v P (b ), so v P (b )+y (b b ) = v P (b )+y b y b = y b v P (b ), which establishes the supergradient inequality. Linear Programming 16

18 Sensitivity Analysis Shadow Prices A similar result holds for the dual: Theorem: v D (a) is convex, and if x solves (P) with objective a, then x v D (a). In this case, v D (a) is convex, the set we support is the epigraph, {(x,µ) : µ f(x)}, and v D (a) is called the subgradient. What is the subgradient inequality? These two theorems explain why dual variables are called shadow prices. They provide directional derivatives for changes in the value of a program with respect to constraint parameters. Notice too that the solution to the primal gives shadow prices for the dual. Linear Programming 17

19 Proof of the Vertex Theorem Choose x C. If x is not a vertex, then for some y 0, x ±y C. Thus Ay = 0, and if x j = 0 then y j = 0. To prove i), let λ solve sup{ λ : x ±λy C. Then x ±λ y C, and one of x ±λ y has more zeros than x. Repeat this argument at most n 1 times to find a vertex in C. Linear Programming 18

20 To prove ii), w.l.o.g. take a y 0. There are two cases: i) a y = 0. W.l.o.g. there is a j such that y j < 0. Note that x k > 0 for any k s.t. y k < 0. Look at x +λy for λ 0. a (x +λy) a x, and A(x +λy) = b. For large λ, x j +λy j < 0, and so x +λy is not in C. Let λ = sup{λ 0 : x +λy C}. Then x +λy C, has at least one more zero than x, and the same value. Repeat at most n 1 times to reach a vertex. ii) a y > 0. If there is a y j < 0, apply the preceding argument. If y 0, then x +λy C for all λ > 0, and so a (x +λy) = a x +λa y, and v P (b) = +, a contradiction. Linear Programming 19