IE 521 Convex Optimization Homework #1 Solution

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1 IE 521 Convex Optimization Homework #1 Solution your NAME here your NetID here February 13, 2019 Instructions. Homework is due Wednesday, February 6, at 1:00pm; no late homework accepted. Please use the provided L A TEX file as a template. You can discuss with others, but please write your own solutions. 1

2 Problem 1: Norms and Dual Norms A real-valued function on R n is called a norm, denoted as, if it satisfies the three properties: (positivity): x R n, x 0; x = 0 if an only if x = 0; (homogeneity): x R n, α R: αx = α x ; (triangle inequality): x, y R n : x + y x + y. The standard norms on R n are the l p -norms (p 1): [Euclidean norm]: p = 2, x 2 := n x2 i ; [Manhattan norm]: p = 1, x 1 := n x i ; ( n ) 1/p x p := x i p [Maximum norm]: p =, x := max 1 i n x i. The dual norm of on R n is defined as x = max y R n : y 1 xt y. Exercise 1.1 (Definition) Prove that is a valid norm. (a) The positivity property is true, because we know x = The homogeneity property is true, because we know max y R n : y 1 xt y x T 0 = 0 αx = max y R n : y 1 αxt y = α max y R n : y 1 xt y = α x The triangle inequality property is true, because we know x + z = max (x + y R n : y 1 z)t y = max y R n : y 1 (xt y + z T y) Let y be the optimal solution of the above. Then, we have x + z = x T y + z T y max y R n : y 1 xt y + max y R n : y 1 zt y = x + z Exercise 1.2 (Example) Denote x p, as the dual norm to the l p -norm (p 1). Show that x p, = x q, where 1 p + 1 q = 1. Hence, 2, = 2, 1, =,, = 1. Hint: Use Hölder s inequality. 2

3 (b) For 1 < p, q <, we have x p, = Now consider the following with Hölder s inequality x T y max y R n : y xt y p 1 x i y i y p x q x q Now the remaining part is to find a y such that the equality can hold. i [1, n]. We calculate Let z i = sign(x i ) x i q 1 for all Further, we calculate Now constuct y = z p p = x i z i = z i p = x i sign(x i ) x i q 1 = p sign(x i) x i q 1 = x i q = x q q x i (q 1)p = z z p, and we have y p = 1. Then we have n x i q = x q q x i y i = x i z i = 1 z p z p x i z i = 1 x q/p q x q q = x q Therefore, x p, = x q For p = 1 and q = or p = and q = 1, they are the trivial cases. The proof is similar but make 1 and be the pairs accordingly. 3

4 Problem 2: Convex Sets Exercise 2.1 (Unit Ball) The unit ball of any norm is the set B = {x R n : x 1}. One can easily see that B is symmetric w.r.t. the origin (x B if and only x B ) and compact (closed and bounded) with nonempty interior. Show that the set B is convex. For any x, y B, and for λ [0, 1]. Then we have λx + (1 λ)y λx + (1 λ)y λ x + (1 λ) y λ + (1 λ) = 1 Therefore, λx + (1 λ)y B. Hence, B is a convex set. Exercise 2.2 (Vice Versa) Let B be convex, symmetric w.r.t. the origin, and compact with nonempty interior. Show that the following function B : x B = inf{t > 0 : x t B} is a valid norm. Moreover, its unit ball is exactly the set B. The positivity property is true, because we know x B = inf{t > 0 : x t B} 0 If x B = 0, then x tb, t > 0, so x = 0. The homogeneity property is true, because we know αx B = inf{t > 0 : αx t B} = inf{αt > 0 : αx αt B} = α inf{t > 0 : x t B} = α x B To show the triangle inequality property is true, it is sufficient to show that This is true because by definition of x B and y B, x + y x B + y B B x x B B and y y B B. Since B is a convex set, Moreover, x + y x B + y B = x B x B + y B x x B + y B x B + y B y y B B B B = {x R n : x B 1} = {x R n : inf{t > 0 : x t = {x R n : x B} B} 1} The last step come from the fact that if x B, then inf{t > 0 : x t B} > 1, which is a contradiction. Exercise 2.3 (Neighborhood of Convex Sets) Let X R n be a convex set and let ɛ > 0. The ɛ-neighborhood of the set X under norm is defined as Prove that X ɛ is a convex set. X ɛ = {x R n : inf x y ɛ}. y X 4

5 Let x and y be two arbitrary elements in X ɛ. Then we have for any ɛ > ɛ, u, v X, s.t. x u ɛ, y v ɛ. For any λ [0, 1], λu + (1 λ)v X, such that [λx + (1 λ)y] [λu + (1 λ)v] λ x u + (1 λ) y b ɛ. This implies that λx + (1 λ)y X ɛ. 5

6 Problem 3: Strong Duality Recall that in class we have shown the [Farkas Lemma] Let A R n m and b R m, exactly one of the two sets must be empty: (i) : {x R n : Ax = b, x 0} (ii) : {y R m : A T y 0, b T y > 0} Exercise 3.1 (Variant of Farkas Lemma) of the following sets must be empty: Prove the following variant of Farkas Lemma: exactly one (i) : {x R n : Ax b} (ii) : {y R m : y 0, A T y = 0, b T y < 0} We first show that system (i) is feasible system (ii) infeasible Suppose there is a x R n such that Ax b, then for any y 0, we have x T (A T y) = (Ax) T y b T y If system (ii) is feasible, then there exists y 0, A T y = 0, b T y < 0, which implies 0 = x T (Ay) b T y < 0 leading to a contradiction. Therefore, (ii) is infeasible. We now show that system (i) infeasible system (ii) feasible We can rewrite Ax b as A(x + x )+s = b, where (x +, x, s) 0. Hence, x + {x : Ax b} x : [ A A I ] x + x + x = b, x 0 s s s By Farkas Lemma, the infeasibility of this system implies that there exists y such that b T y < 0, [ A A I ] T y 0 This implies that y 0, A T y = 0, b T y < 0. Namely, system (ii) is feasible. Overall, we know exactly one of them is empty. Exercise 3.2 (Duality of Linear Program) The above lemma can be used to derive strong duality of linear programs. Consider the primal and dual pair of linear programs: min x c T x s.t. Ax = b, x 0 (P ) max y b T y s.t. A T y c (D) Assume that the problem (P ) has an optimal solution x with finite optimal value p. (a) Let y be a feasible solution to (D), show that b T y p. (b) Apply the above variant Farkas lemma to show that the following system {y : A T y c, y T b p } is nonempty, namely, there exists y that satisfies [ ] [ ] A T c b T y. p 6

7 (a) Let y be a feasible solution to (D), then A T y c. Hence, b T y = (Ax ) T y = x T (A T y) x T c = p. [ λ (b) Suppose the above system is not feasible. By the variant of Farkas Lemma, λ = 0, such λ0] that { Aλ λ 0 b = 0 λ T c λ 0 p < 0 If λ 0 > 0, then A( λ λ 0 ) = b and c T ( λ λ 0 ) < p, contradicts with p being optimal If λ 0 = 0, then Aλ = 0, λ T c < 0. then we can construct a feasible solution x = x + λ that leads to small objective, c T x = c T (x + λ) < c T x = p which contradicts with the optimality of x. Therefore, the assumption is not true, i.e. above system must be feasible. Combining (a) and (b), we can see that if problem (P) is solvable, then the dual problem (D) is also solvable, and the optimal values are the same. 7

8 Problem 4: Helley s Theorem and Duality Exercise 4.1 (Special Case of Helley s Theorem) Using linear programming duality, prove Helleys theorem for the special case when the convex sets are halfspaces. When the convex sets are halfspaces, the equivalent statement is as following. If m halfspaces in R n, m > d, do not have a point in common, then there exist some d + 1 of them that do not have a point in common either. Let s define the m halfspaces by Ax b do not have a point in common, then that means that the following linear program has no solution: max 0 s.t. Ax b By strong duality, its dual problem is either infeasible or unbounded: min b T y s.t. A T y = 0, y 0 Since we know y = 0 is feasible for the dual, then the dual must be unbounded. With respect to the simplex method, an unbounded problem means that there exists some basic feasible solution with an entering variable but no leaving variable(refer section 3.2 in introduction to Linear Optimization) Assume, without loss of generality, that y 1, y 2, y 3,..., y d are the basic variables for this basic feasible solution. As a reminder, there are d of these because the dual has d constrains. Assume that y d+1 is the entering variable for which there is no leaving variable. By increasing the value of y d+1 enough we can obtain a solution y such that: 1. y is feasible 2. b T y < 0 3. The remaining non-basic variable y d+2 = y d+3 =... = y m = 0. Now, let A T D, y D, and b D denote the restriction of A T, y, and b, respectively, to the variables y 1, y 2,..., y d+1. Because the remaining non-basic variable is zero, we havea T D y D = 0 and b T D y D < 0.(y D 0 as well) Finally, consider the intersection of d + 1 halfspaces defined by A D x b D. If there exists an x in this intersection, then x T A T D b T D x T A T Dy D b T Dy D 0 b T Dy D which is a contradiction. Therefore, the intersection of d + 1 halfspaces defined by A D x b D is empty, and so we have found a set of d + 1 of the m halfspaces that do not have a point in common. 8