AMS : Combinatorial Optimization Homework Problems - Week V

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1 AMS : 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 equations, i.e., {x R n : Ax = b} is an affine subspace of R n. A polytope is a bounded polyhedron. 1. Show the following: (i) The intersection of an affine subspace with a polyhedron is a polyhedron. Express the original polyhedron as P = {x : Ax b} and the sffine subspace as L = {x : A x = b }. The intersection P L = {x : A x = b, Ax b} = {x : A x b, A x b, Ax b} which is by definition a polyhedron. (ii) Let P 1, P 2 be two polytopes. Define C = {x + y : x P 1, y P 2 }. Show that C is a polytope. Express P 1 = conv{x 1,..., x p } and P 2 = conv{y 1,..., y q }. We claim that C = conv{x i + y j : i {1,..., p}, j {1,..., q}}. Consider z C, then z = x + y for some x P 1 and y P 2. We can write x = p λ ix i and y = q µ jy j, where λ i 0, p λ i = 1, µ j 0 and q µ j = 1. Observe that λ iµj = 1. Also, λ i µ j (x i + y j ) = p q λ i x i + µ j y j = x + y = z. Since λ iµ j (x i + y j ) conv{x i + y j : i {1,..., p}, j {1,..., q}}, we have z conv{x i + y j : i {1,..., p}, j {1,..., q}}. Therefore, C conv{x i + y j : i {1,..., p}, j {1,..., q}}. To show the reverse inclusion, consider z conv{x i + y j : i {1,..., p}, j {1,..., q}}; so we can write z = σ ij(x i + y j ) where σ ij 0 and σ ij = 1. We now simply this expression: σ ij (x i +y j ) = σ ij x i + σ ij y j = q p ( σ ij )x i + ( σ ij )y j = λ i x i + µ j y j, where λ i = q σ ij and µ j = p σ ij. Observe that p λ i = σ ij = 1. Similarly, q µ j = 1. Thus, p λ ix i P 1 and q µ jy j P 2. z = p λ ix i + q µ jy j P 1 + P 2. Thus, we express C as the convex hull of a finite set of points; by the Minkowski-Weyl theorem it is a polytope. (iii) The intersection of two polyhedra is a polyhedron. Thus, show that the intersection of a polyhedron with a polytope is a polytope. Let one polyhedron be represented by A 1 x b 1 and the second polyhedron be A 2 x b 2. The intersection is given by A 1 x b 1, A 2 b 2 which is another polyhedron. Since the intersection of a bounded set set with another set is also bounded, the intersection of a polytope with another polyhedron is a polytope. 1

2 (iv) Let T : R n R d be a linear map. Show that if P R n is a polytope, then T (P ) is a polytope in R d. We represent P as the convex hull conv{v 1,..., v t }. We claim that T (P ) = conv{t v 1,..., T v t } because of the following sequence of equivalences. y conv{t v 1,..., T v t } y = λ 1 T v λ t T v t y = T (λ 1 v λ t v t ) y T (P ) Thus, we express T (P ) as the convex hull of a finite set of points; by the Minkowski-Weyl theorem it is a polytope. 2. (Complementary slackness) Let x R n be an optimal solution to the problem max{c T x : Ax b} and y R m be an optimal solution to the problem max{y T b : y T A = c T, y 0}. Show that for every i = 1..., m either a i x = b i or y i = 0 (or both). (Here a i denotes the i-th row of A and b i is the i-th component of b.) [Hint: consider the vector (y ) T (Ax b)] The theorem is saying that in an optimal primal-dual pair of solutions for an LP, either a constraint is tight at the optimal primal solution or the corresponding dual multiplier is 0. We will in fact show that two feasible solutions x (for the primal) and y (for the dual) are optimal solutions if and only if for every i = 1..., m either a i x = b i or y i = 0 (or both). Observe that since x and y are feasible for the primal and dual problems respectively, c T x (y ) T b = ((y ) T A)x (y ) T b = (y ) T (Ax b) 0 Moreover, since each component of y is nonnegative and each component of Ax b is also nonnegative, c T x (y ) T b = 0 if and only if for every i = 1..., m either a i x = b i or y i = 0 (or both). Finally, c T x (y ) T b = 0 is equivalent to x and y being optimal solutions to the primal and dual problems respectively. 3. Find an example of a pair of primal and dual linear programs such that both problems are infeasible. [Hint: There is an example with 2 variables in each problem] Primal: Dual: max x 1 subject to x 1 + x 2 1 x 2 0 x 2 1 max y 1 y 3 subject to y 1 = 1 y 1 + y 2 y 3 = 0 y 1, y 2, y Let P = {x R n : Ax b} be a nonempty polyhedron. Let C = {r R n : x + λr P for all x P, λ R + } where R + is the set of nonnegative real numbers. Show that (i) C is a convex cone. [This cone is called the recession cone of the polyhedron P ] (ii) C = {r R n : Ar 0}. (i) We need to first verify that C is convex. Consider any r 1, r 2 C and any µ [0, 1] and let r = µr 1 + (1 µ)r 2. For any x P, λ 0 x + λ r = x + λµr 1 + λ(1 µ)r 2. Since r 1 C, 2

3 x + λµr 1 P and since r 2 C, (x + λµr 1 ) + λ(1 µ)r 2 P. A very similar argument shows that C is a cone. (ii) Consider any r such that Ar 0. Then A(x + λr) = Ax + λar Ax for λ 0 and Ax b if x P. Thus, A(x + λr) b for all x P and λ 0. This shows that {r R n : Ar 0} C. Consider any r such that Ar b. This means, for some i = 1,..., m, a i r > 0. Since P is nonempty, let x P. Since a i r > 0, there exists λ 0 such that a i (x + λr) = a i x + λ(a i r) > b i. This shows that r C. Hence C {r R n : Ar 0}. 5. Let P = {x R n : Ax b} be a nonempty polyhedron. Let L = {r R n : x + λr P for all x P, λ R}. Show that (i) L is a linear subspace of R n. [This is called the lineality space of the polyhedron P ] (ii) L = {r R n : Ar = 0}. (i) For any r 1, r 2 L, x + λ(r 1 + r 2 ) = x + λr 1 + λr 2. Since r 1 L, x + λr 1 P and since r 2 L, (x + λr 1 ) + λr 2 P. Thus r 1 + r 2 L. Similarly, for any r L and µ R, x + λ(µr) = x + (λµ)r P for any lambda R since r L. Thus, µr L. Therefore, L is a linear subspace. (ii) Proof is similar to the case of the cone. 6. Using the notation from Problems 5 and 6, show that L = C ( C). We say that P is pointed if L = {0}. Suppose P ; show that P has at least one vertex if and only if P is pointed. C ( C) = {r R n : Ar 0} {r R n : A( r) 0} = {r R n : Ar 0, Ar 0} = {r R n : Ar = 0} = L. If P has a vertex z, then A z has rank n, thus A has rank n. Therefore Ar = 0 only has the 0 solution, and so L = {0}. So if P has a vertex, it is pointed. Assume P is pointed. So Ar = 0 has only the 0 solution and so A has rank n. Let z P since P is nonempty. If A z has rank n, then z is a vertex and we are done. Else, consider any r such that A z r = 0. We know that A has rank n, and thus there exists a row a i such that a i r 0. By choosing r or r, we can make sure a i r > 0. Then let ɛ = min {b i a i z i {1,...,m} a i r : i T z, a i r > 0} (1) (recall that T z is the index set of the rows in A z.) Let z = z + ɛr. Observe that A z z = A z (z + ɛr) = A z z = b z. Moreover, let i be the index which achieves the minimum in (1). Thus, a i z = a i (z + ɛr) = a i z + ( b i ai z a i r )(a i r) = b i. Thus, T z T z {i }. Since a i r > 0, rank(a z ) > rank(a z ). Continuing in this manner, we can find a sequence of points v in P with strictly increasing rank(a v ). Hence, we will at some point find a point v P where A v has rank n. At that point, v would be a vertex of P. 7. (The Diet Problem) Suppose you want to design a diet for your meals. You have certain food items (e.g., spinach, chicken, rice etc.); let us label these different food types as f 1,... f n. You can choose any nonnegative amount of a food item to put in your diet. Each food item has a per unit cost c 1,..., c n associated with it. You have to meet some nutritional constraints: for example, you must have at least 5g of protein, and at most 40g of protein in the meal. Let 3

4 us say there are {1,..., k} nutritional categories and each category has a lower bound l i and an upper bound u i that must be met. Suppose that each unit of food item f j provides a ij units of nutritional category i. How will you solve the problem of designing a diet satisfying the nutritional demands that has the least cost? Let x 1,..., x n denote the amounts of food items f 1,... f n in the best diet. Then, to satisfy the nutritional constraints for category i {1,..., k}, we must have a ij x j u i, a ij x j l i Thus, we will have 2k constraints. The objective to minimize is n c jx j. This is now a linear program (in the form max{c T x : Ax b} discussed in class) with A = (a ij ), b = (u 1,..., u k, l 1,..., l k ) and c = ( c 1,..., c n ). 8. (Linear Regression with different objectives) In linear regression, we have a bunch of labeled data points z 1,..., z k R n with real valued labels y 1,..., y k. We want to fit the best linear function to this labeled data. More precisely, we want to find parameters β = (β 1,..., β n ) so as to minimize the errors y j n β iz j i. The typical objective is the sum of the squares of the errors, i.e., we wish to minimize k (y j n β iz j i )2. Suppose we are interested in the following variant: Firstly, we don t want to allow arbitrary values of the parameters; we want more controlled regression. Suppose for each parameter β i, we have certain preset upper and lower bounds u i and l i respectively that we want the parameter to lie within. Also, instead of minimizing the sum of squares, suppose we want to minimize the sum of the absolute values, i.e., minimize k y j n β iz j i subject to these bound constraints on the parameter values (l 1 minimization). How would you solve this problem? What if you were interested in minimizing the largest error, i.e., minimize max j {1,...,k} y j n β iz j i (l minimization)? Let β 1,..., β n be the parameter values. The bound constraints are easy: β i u i, β i l i To model the objective k y j n β iz j i, we introduce new auxiliary variables t 1,..., t k and impose the constraints: for every j = 1,..., k. minimize y j β i z j i t j, y j + β i z j i t j, This forces y j n β iz j i t j for all j = 1,..., k. If we now k subject to these constraints, then it is not hard argue that the t j values will be pushed down to exactly y j n β iz j i, and out problem will be solved. For the l version, we introduce a single auxiliary variable t, and impose the constraints: y j t j β i z j i t, y j + 4 β i z j i t,

5 for every j = 1,..., k. This forces y j n β iz j i t for all j = 1,..., k. Now minimizing t subject to these constraints will force t to take the value of the maximum of y j n β iz j i, which is exactly what we want. 5