Lecture 19 Algorithms for VIs KKT Conditions-based Ideas. November 16, 2008

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1 Lecture 19 Algorithms for VIs KKT Conditions-based Ideas November 16, 2008

2 Outline for solution of VIs Algorithms for general VIs Two basic approaches: First approach reformulates (and solves) the KKT conditions directly (as a complementarity problem) - useful when K has a complex algebraic chaarcterization Second approach is inherently a primal framework that uses an appropriately defined merit function - a good idea when K is simple (next and last lecture) Game theory: Models, Algorithms and Applications 1

3 KKT Conditions based Methods Consider the VI(K,F) which requires an x such that (x x ) T F (x ) 0, x K. Suppose K is finitely representable as K = {x R n : h(x) = 0, g(x) 0}, where h : R n R l and g : R n R m are continuously differentiable Then the KKT conditions for VI(K,F) are (KKT) F (x) + JH(x) T µ + Jg(x) T λ = 0 h(x) = 0 0 g(x) λ 0. For the remainder of this section we assume that F C 1 on R n h, g C 2 on R n Game theory: Models, Algorithms and Applications 2

4 Using the FB function A reformulation of (KKT) that uses the FB function is given by L(x, µ, λ) h(x) 0 = Φ FB (x, µ, λ) ψ FB ( g 1 (x), λ),. ψ FB ( g m (x), λ) where L(x, µ, λ) F (x) + JH(x) T µ + Jg(x) T λ is the VI-Lagrangian function The natural merit function is given by By the results of the last 2 lectures: θ FB (x, µ, λ) 1 2 Φ FB(x, µ, λ) T Φ FB (x, µ, λ). Φ FB and θ FB are semismooth and continuously differentiable, respectively on R n+l+m. Moreover if JF, 2 g i and 2 h i are locally Lipschitz then Φ FB is strongly semismooth Game theory: Models, Algorithms and Applications 3

5 Nonsingularity of elements of Generalized Clarke Jacobian Next, we discuss the nonsingularity of all elements in Φ FB (x, µ, λ) We also relate these to the strong regularity of the KKT system given by (KKT) Proposition 1 (10.1.1) The generalized Clarke Jacobian Φ FB (x, µ, λ) is contained in the following family of matrices: J x L(x, µ, λ) Jh(x) T Jg(x) T A(x, µ, λ) { Jh(x) 0 0 }, D g (x, λ)jg(x) 0 D λ (x, λ) where D λ (x, λ) = diag(a 1 (x, λ),..., a m (x, λ)) and D g (x, λ) = diag(b 1 (x, λ),..., b m (x, λ)) are m m diagonal matrices whose diagonal elements are given by (λ i, g i (x)) g (a i (x, λ), b i (x, λ)) 2 (1, 1), (g i (x), λ i ) (0, 0) i (x)+λ2 i clb(0, 1) (1, 1), (g i (x), λ i ) = 0. Game theory: Models, Algorithms and Applications 4

6 Moreover, A(x, µ, λ) is a linear Newton approximation of Φ FB at (x, µ, λ) which is strong if JF and each g 2 i and 2 h j are locally Lipschitz continuous at x. Proof: Omitted (follows easily from results in Ch. 7). Game theory: Models, Algorithms and Applications 5

7 Index sets of relevance Next we introduce several index sets that will aid in the discussion: I 0 {i I : g i (x) = 0 λ i } I < {i I : g i (x) < 0 = λ i } I R I\(I 0 I < ) Note that i I R if and only if one of the following hold: g i (x) > 0 or infeasibility of g λ i < 0 or infeasibility of multipliers ( g i (x), λ i ) > 0. no complementarity If 0 g i (x) λ i 0, then I R is empty. More generally ψ FB ( g i (x), λ) = 0 i I 0 I <. Game theory: Models, Algorithms and Applications 6

8 We may also define I 00, I + I 0 given by I 00 = {i I 0 : λ i = 0} I + = {i I 0 : λ i > 0}. no strict complementarity strict complementarity We further refine I 00 with the particular elements a i (x, λ), b i (x, λ) of the generalized Clarke Jacobian Φ FB (x, µ, λ) : I 01 = {i I 00 : a i (x, λ) = 0} I 02 = {i I 00 : max(a i (x, λ), b i (x, λ)) < 0} I 03 = {i I 00 : b i (x, λ) = 0} If we let I R2 I R I 02, then the following relationships hold: I = I 0 I < I R (the whole set of indices) I 0 = I 00 I + I 00 = I 01 I 02 I 03 implying that I = I + I 01 I R2 I 03 I < Recall that (ai, b i ) cl(b(0, 1) (1, 1) Game theory: Models, Algorithms and Applications 7

9 Moreover we have (D g ) ii = 0 and (D λ ) ii = 1, i I < I 03 ( from definition) i I < = a i = 1 = (D λ ) ii = 1 i I 03 = b i = 0 = (D g ) ii = 0 Similarly, we have (D g ) ii = 1 and (D λ ) ii = 0, i I + I 01 max((d g ) ii, (D λ ) ii ) < 0, i I R2 = I R I 02. Useful to define a family of matrices closely related to the strong stability conditions. For any subset J I 00 I R, J x L Jh T Jg+ T JgT J Jh M F B (J ) Jg Jg J Game theory: Models, Algorithms and Applications 8

10 Equivalent statement of strong stability Lemma 1 (10.1.2) A KKT triple (x, µ, λ ) is a strongly-stable KKT point of VI(K,F) if and only if for all subsets J I 00, the determinants of the matrices M F B (J ) have the same nonzero sign. Proof: ( ) Suppose that (x, µ, λ ) is a strongly stable KKT triple. By corollary , it follows that JL Jh T Jg+ T B Jh 0 0 Jg is nonsingular and the Schur complement C Why do we only need to restrict ourselves to I00? ( ) Jg B 1 Jg T Game theory: Models, Algorithms and Applications 9

11 is a P matrix. Let J be an arbitrary subset of I 00. By the Schur determinantal formula, we have det M F B (J ) = det B det(m F B (J )/B). The Schur complement (M F B (J )/B) is a principal submatrix of C. But C is a P-matrix implying that every principal submatrix has a positive determinant or det(m F B (J )/B) > 0. But this implies that sgn det M F B (J ) = sgn det B and is independent of J. ( ) Omitted. This suggests a possible definition for an arbitrary triple (x, µ, λ). If this triple is a KKT triple, then it is equivalent to the earlier definition. The ESSC defined below extends this to the case of strong-stability of a non-kkt triple. Game theory: Models, Algorithms and Applications 10

12 Extended Strong-Stability Condition (ESSC) Definition 1 (10.1.3) The Extended Strong-Stability Condition (ESSC) is said to hold at a triple (x, µ, λ) R n+l+m if (a) sgn det M F B (J ) is a nonzero constant for all index sets J I 00 ; (b) det M F B (J ) det am F B (J ) 0 for any two index set J and J that are subsets of I 00 I R. Equivalently, the ESSC holds at (x, µ, λ) if and only if (i) The matrix is nonsingular B = J x L Jh T Jg+ T Jh 0 0 = M F B ( ) Jg (ii) the M F B (J ) is nonsingular for all nonempty subsets J I 00. (iii) for all subsets J I 00 I R for which which M F B (J ) is nonsingular, we have sgn det M F B (J ) = sgn det B. Game theory: Models, Algorithms and Applications 11

13 By considering the nonsingularity of the matrix M F B (I 00 ), it follows that if ESSC holds at (x, µ, λ) then the gradients are linearly independent. { h j, j = 1,..., l} { g i : i I 0 } Essentially, the ESSC is a sufficient condition for the matrices in the generalized Jacobian to be nonsingular. Theorem 1 Suppose the ESSC holds at a triple (x, µ, λ). All the elements in A(x, µ, λ) (and therefore all matrices in the generalized Jacobian Φ FB (x, µ, λ)) are nonsingular. Proof sketch: Consider an arbitrary matrix H in A(x, µ, λ). By definition, such a Game theory: Models, Algorithms and Applications 12

14 matrix has the following structure (by breaking into index sets): J x L Jh T Jg+ T JgT 01 JgR2 T Jg03 T JgT < Jh Jg H = Jg , (D g ) R2 Jg R (D λ ) R I I where (D g ) R2 and (D λ ) R2 are negative definite diagonal matrices. Moreover H is Game theory: Models, Algorithms and Applications 13

15 nonsingular if the following matrix is nonsingular: J x L Jh T Jg+ T JgT 01 JgR2 T Jh H = Jg Jg Jg R (D g ) 1 R2 (D λ) R2 J x L Jh T Jg+ T JgT 01 JgT R Jh = Jg Jg Jg R (D g ) 1 R2 (D λ) R2 = M F B ({I 01 I R2 }) + D. Next, we employ the following determinantal formula: For any square matrix A of order r and any diagonal matrix D of the same order, det(d + A) = α det D αα det Aᾱᾱ, where the summation ranges for all subsets α of {1,..., r} with complements ᾱ. Game theory: Models, Algorithms and Applications 14

16 It follows that det H = det(((d g ) 1 R2 (D λ) R2 ) αα det M F B (J ) α α, α I R2 where the summation is over all subsets α of I R2 and α J \α. The final step requires invoking the ESSC to show that this determinantal form is indeed positive. (omitted) Game theory: Models, Algorithms and Applications 15

17 Necessity of ESSC We see that ESSC is a sufficient condition for nonsigularity of the elements in the generalized Jacobian. If the triple satisfies the KKT conditions, then the ESSC is also necessary: Theorem 2 Let (x, µ, λ ) be a KKT triple of K, F ). equivalent: Then the following are (a) The ESSC holds at (x, µ, λ ). (b) All matrices in A(x, µ, λ ) are nonsingular. (b) All matrices in Φ FB (x, µ, λ ) are nonsingular. Proof: From the earlier result, we have (a) = (b) = (c). It suffices to prove that (c) = (a). We assume that all the matrices in Φ FB (x, µ, λ ) are nonsingular. show that the set of gradients We first { g i (x ) : i I 00 } Game theory: Models, Algorithms and Applications 16

18 are linearly independent. Define a sequence {λ k } as λ k i λ i, if i I\I 00 1/k, if i I 00 The sequence {λ k } converges to λ ; in addition for each k, Φ FB is continuously differentiable at (x, µ, λ k ). The sequence of matrices {JΦ FB (x, µ, λ k ) converges to a matrix H P B(x, µ, λ ) with I 01 = I 00 and I 02 = I 03 =. The matrix H is nonsingular by definition and the linear independence of the gradients follows (see earlier proof.) Next, we show that for any subset J I 00, there exists an element in Φ FB (x, µ, λ ) such that I 01 = J, I 02 = and I 03 = I 00 \J. Since the gradients are linearly independent, the classical implicit function theorem implies that there is a sequence {x k } converging to x such that g i (x k ) > 0 for all i I 00, k. For each k, we define λ i if i I\I 00 λ k i gi (x k ) if i J g i (x k ) 2 if i I 00 \J. Game theory: Models, Algorithms and Applications 17

19 The sequence {(x k, µ, λ k )} converges to (x, µ, λ ) and Φ FB is continuously differentiable at (x k, µ, λ k ) since there is no index i such that g i (x k ) = λ k i = 0. By using prop and the continuity of the involved functions, it follows that {JΦ FB (x k, µ 8, λ k )} converges to an H with the desired properties. Proposition 2 (7.1.18) Let G : R n R p R n be Lipschtiz in nbhd of ( x, ȳ) for which G( x, ȳ) = 0. Assume that all matrices in Π x G( x, ȳ) are nonsingular.then there exist open nbhds U and V of x and ȳ, respectively such that for every y V, the equation G(x, y) = 0 has a unique solution x = F (y) U, F (ȳ) = x and the map F : V U is Lipschtiz. Since all the matrices in Φ FB (x, µ, λ ) (a convex set) are nonsingular, it follows that such matrices have the same nonzero determinantal sign. Suppose not. Then if H 1 and H 2 are two matrices in Φ FB such that det H 1 < 0 and det H 2 > 0. Then there exists an H such that det H = 0 where H is a convex combination of H 1 and H 2. This contradicts the nonsingularity of all elements in Φ FB (x, µ, λ ). To complete the proof, we need so to show that the matrices M F B (J ) have the same nonzero determinantal sign for all J I 00. For each index set, let H(J ) Φ FB (x, µ, λ ) such that J = I 01 and I 03 = I 00 \I 01. From the Game theory: Models, Algorithms and Applications 18

20 structure of H(J ), we have JL Jh T Jg+ T JgT J sgn det H(J ) = ( 1) m Jh sgn det Jg = ( 1) m sgn det M F B (J ) Jg J 0 0 Since all the matrices in P B(x, µ, λ ) have determinants of the same nonzero sign, the ESSC holds. Corollary 1 A KKT triple (x, µ, λ ) is strongly stable if and only all the matrices in Φ FB (x, µ, λ ) are nonsingular. Game theory: Models, Algorithms and Applications 19

21 Example Let n = 1, l = 0, m = 1 and g 1 (x) x. Thus the VI is a 1-dimensional LCP(q,M). Case 1: q = -1, M = 1 = F (x) x 1. Then ( ) x 1 λ Φ FB (x, λ) =. x 2 + λ 2 x λ Consider the KKT pair (0,0) implying that I 00 = {1}, I + = I R =. Moreover, M F B ( ) = 1 and ( ) 1 1 M F B ({1}) = 1 0 implying that ESSC is satisfied. From the expression of Φ FB, the elements of Φ FB (0, 0) are given by ( 1 1 a 1 b 1 ), a 2 + b 2 1. These matrices are nonsingular (can be seen by showing that the determinant is nonzero). Game theory: Models, Algorithms and Applications 20

22 Case 2: q = 0, M = 1, F (x) = x Then ( ) x λ Φ FB (x, λ) =. x 2 + λ 2 x λ Consider the KKT pair (0,0) implying that I 00 = {1}, I + = I R =. Moreover, M F B ( ) = 1 and ( ) 1 1 M F B ({1}) = 1 0 implying that ESSC is not satisfied (since the signs of the determinants are not the same). From the expression of Φ FB, the elements of Φ FB (0, 0) are given by ( ) 1 1, a 2 + b 2 1. a 1 b 1 Singularity of this matrix follows when a = b = 1/ 2. Game theory: Models, Algorithms and Applications 21

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