AND. Dimitri P. Bertsekas and Paul Tseng


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1 SET INTERSECTION THEOREMS AND EXISTENCE OF OPTIMAL SOLUTIONS FOR CONVEX AND NONCONVEX OPTIMIZATION Dimitri P. Bertsekas an Paul Tseng Math. Programming Journal, 2007
2 NESTED SET SEQUENCE INTERSECTIONS Basic Question: Given a neste sequence of nonempty close sets {S k } in R n (S k+1 S k for all k), when is k=0 S k nonempty?
3 NESTED SET SEQUENCE INTERSECTIONS Basic Question: Given a neste sequence of nonempty close sets {S k } in R n (S k+1 S k for all k), when is k=0 S k nonempty? Set intersection theorems are significant in at least four major contexts: Existence of optimal solutions Preservation of closeness by linear transformations Duality gap issue, i.e., equality of optimal values of the primal convex problem an its ual minimize x X, g(x) 0 f(x) { maximize µ 0 q(µ) inf f(x) + µ g(x) } x X minmax = maxmin issue, i.e., whether min x max z φ(x, z) = max z min x φ(x, z), where φ is convex in x an concave in z
4 SOME SPECIFIC CONTEXTS I Does a function f : R n (, ] attain a minimum over a set X? This is true iff the intersection of the nonempty sets { x X f(x) γ } is nonempty Level sets of f X
5 SOME SPECIFIC CONTEXTS I Does a function f : R n (, ] attain a minimum over a set X? This is true iff the intersection of the nonempty sets { x X f(x) γ } is nonempty Level sets of f X If C is close, is AC close? N k x S k C y W k y k+1 y k AC Many interesting special cases, e.g., if C 1 an C 2 are close, is C 1 + C 2 close?
6 SOME SPECIFIC CONTEXTS II Preservation of closeness by partial minima: If F (x, u) is close, is p(u) = inf x F (x, u) close? Critical question in the uality gap issue, where F (x, u) = { f(x) if x X, g(x) u, otherwise an p is the primal function. Critical question regaring minmax=maxmin where F (x, u) = { supz Z { φ(x, z) u z } if x X, if x / X. We have minmax=maxmin if is close. p(u) = inf F (x, u) x Rn Can be aresse by using the relation Proj ( epi(f ) ) epi(p) cl (Proj ( epi(f ) ))
7 ASYMPTOTIC DIRECTIONS Given a sequence of nonempty neste close sets {S k }, we say that a vector 0 is an asymptotic irection of {S k } if there exists {x k } s. t. x k S k, x k 0, k = 0, 1,... x k, x k x k. S 0 S 1 x 1 S 2 x 0 S 3 x 2 x 3 x 4 x 5 x 6 Asymptotic Sequence 0 Asymptotic Direction
8 ASYMPTOTIC DIRECTIONS Given a sequence of nonempty neste close sets {S k }, we say that a vector 0 is an asymptotic irection of {S k } if there exists {x k } s. t. x k S k, x k 0, k = 0, 1,... x k, x k x k. S 0 S 1 x 1 S 2 x 0 S 3 x 2 x 3 x 4 x 5 x 6 Asymptotic Sequence 0 Asymptotic Direction A sequence {x k } associate with an asymptotic irection as above is calle an asymptotic sequence corresponing to. Generalizes the known notion of asymptotic irection of a set (rather than a neste set sequence).
9 RETRACTIVE ASYMPTOTIC DIRECTIONS An asymptotic sequence {x k } an corresponing asymptotic irection are calle retractive if there exists k 0 such that x k S k, k k. {S k } is calle retractive if all its asymptotic sequences are retractive. x 1 x 2 x 3 x 0 x 4 x 5 Asymptotic Sequence 0 Asymptotic Direction S 2 S 1 S 0 S 1 0 S 2 x 2 x 1 x 0 S 0 0 x 2 x 1 x 0 (a) (b)
10 RETRACTIVE ASYMPTOTIC DIRECTIONS An asymptotic sequence {x k } an corresponing asymptotic irection are calle retractive if there exists k 0 such that x k S k, k k. {S k } is calle retractive if all its asymptotic sequences are retractive. x 1 x 2 x 3 x 0 x 4 x 5 Asymptotic Sequence 0 Asymptotic Direction S 2 S 1 S 0 S 1 0 S 2 x 2 x 1 x 0 S 0 0 x 2 x 1 x 0 (a) Important observation: A retractive asymptotic sequence {x k } (for large k) gets closer to 0 when shifte in the opposite irection. (b)
11 SET INTERSECTION THEOREM Proposition: The intersection of a retractive neste sequence of close sets is nonempty. Key proof ieas: (a) Consier x k a minimum norm vector from S k. (b) The intersection k=0 S k is empty iff {x k } is unboune. (c) An asymptotic sequence {x k } consisting of minimum norm vectors from the S k cannot be retractive, because {x k } eventually gets closer to 0 when shifte opposite to the asymptotic irection. () Hence {x k } is boune. x 1 x 2 x 3 x 0 x 4 x 5 Asymptotic Sequence 0 Asymptotic Direction
12 CALCULUS OF RETRACTIVE SEQUENCES Unions an intersections of retractive set sequences are retractive. Polyheral sets are retractive. Recall the recession cone R C of a convex set C, an its lineality space L C = R C ( R C ). x + a y x y 0 Recession Cone R C Convex Set C For S k :convex, the set of asymptotic irections of {S k } is the set of nonzero k R Sk.
13 CALCULUS OF RETRACTIVE SEQUENCES Unions an intersections of retractive set sequences are retractive. Polyheral sets are retractive. Recall the recession cone R C of a convex set C, an its lineality space L C = R C ( R C ). x + a y x y 0 Recession Cone R C Convex Set C For S k :convex, the set of asymptotic irections of {S k } is the set of nonzero k R Sk. The vector sum of a compact set an a polyheral cone (e.g., a polyheral set) is retractive. The level sets of a continuous concave function {x f(x) γ} are retractive.
14 EXISTENCE OF SOLUTIONS ISSUES Stanar results on existence of minima of convex functions generalize with simple proofs using the set intersection theorem. Use the set intersection theorem, an existence of optimal solution <=> nonemptiness of (nonempty level sets) Example 1: The set of minima of a close convex function f over a close set X is nonempty if there is no asymptotic irection of X that is a irection of recession of f. Example 2: The set of minima of a close quasiconvex function f over a retractive close set X is nonempty if A R L, where A: set of asymptotic irections of X, an γ k f. R = k=0 R S k, L = k=0 L S k, S k = { x f(x) γ k }
15 LINEAR AND QUADRATIC PROGRAMMING FrankWolfe Th: Let X be polyheral an f(x) = x Qx + c x where Q is symmetric (not necessarily positive semiefinite). If the minimal value of f over X is finite, there exists a minimum of f of over X. The proof is straightforwar using the set intersection theorem, an existence of optimal solution <=> nonemptiness of (nonempty level sets)
16 LINEAR AND QUADRATIC PROGRAMMING FrankWolfe Th: Let X be polyheral an f(x) = x Qx + c x where Q is symmetric (not necessarily positive semiefinite). If the minimal value of f over X is finite, there exists a minimum of f of over X. The proof is straightforwar using the set intersection theorem, an existence of optimal solution <=> nonemptiness of nonempty level sets. Extensions not covere: X can be the vector sum of a compact set an a polyheral cone. f can be of the form f(x) = p(x Qx) + c x where Q is positive semiefinite an p is a polynomial. These extensions nee the subsequent theory. Reason is that level sets of quaratic functions (an polynomial) are not retractive.
17 MULTIPLE SEQUENCE INTERSECTIONS Key question: Given {Sk 1} an {S2 k }, each with nonempty intersection by itself, an with S 1 k S2 k Ø, for all k, when oes the intersection sequence {Sk 1 Sk 2 } have an empty intersection? Sk 1 S2 : Critical Asymptote Examples inicate that the trouble lies with the existence of a critical asymptote. Critical asymptotes roughly are: Common asymptotic irections such that starting at k Sk 2 an looking at the horizon along, we o not meet k Sk 1 (an similarly with the roles of Sk 1 an S2 k reverse).
18 CRITICAL DIRECTIONS We say that an asymptotic irection of {S k }, with k S k Ø is a horizon irection with respect to a set G if for every x G, we have x + α k S k for all α sufficiently large. We say that an asymptotic irection of {S k } is noncritical with respect to a set G if it is either a horizon irection with respect to G or a retractive horizon irection with respect to k S k. Otherwise, is critical with respect to G. Horizon with respect to Rn Retractive
19 CRITICAL DIRECTIONS We say that an asymptotic irection of {S k }, with k S k Ø is a horizon irection with respect to a set G if for every x G, we have x + α k S k for all α sufficiently large. We say that an asymptotic irection of {S k } is noncritical with respect to a set G if it is either a horizon irection with respect to G or a retractive horizon irection with respect to k S k. Otherwise, is critical with respect to G. Horizon with respect to Rn Retractive Example: The asymptotic irections of a level set sequence of a convex quaratic S k = {x x Qx + c x + b γ k }, γ k 0, are noncritical with respect to R n. (Extension: Convex polynomials, biirectionally flat convex fns.) Example: The as. irections of a vector sum S of a compact an a polyheral set are noncritical (are retractive hor. ir. with resp. to S).
20 EXAMPLE OF CRITICAL DIRECTION Sk 1 S2 : Critical Asymptote Two set sequences, all intersections of a finite number of sets are nonempty. shown is the only common asymptotic irection. is noncritical for S 2 with respect to k S 1 k (because it is retractive). is critical for k S 1 k with respect to S2.
21 CRITICAL DIRECTION THEOREM Roughly it says that: For the intersection of a set sequence {Sk 1 S2 k Sr k } to be empty, some common asymptotic irection must be critical for one of the {S j k } with respect to all the others. Critical Direction Theorem: Consier {Sk 1} an {Sk 2 }, each with nonempty intersection by itself. If S 1 k S2 k Ø for all k, an k=0 (S1 k S2 k ) = Ø, there is a common asymptotic irection that is critical for {S 1 k } with respect to k S 2 k (or for {S2 k } with respect to k S 1 k ). Extens to any finite number of sequences {S j k }.
22 CRITICAL DIRECTION THEOREM Roughly it says that: For the intersection of a set sequence {Sk 1 S2 k Sr k } to be empty, some common asymptotic irection must be critical for one of the {S j k } with respect to all the others. Critical Direction Theorem: Consier {Sk 1} an {Sk 2 }, each with nonempty intersection by itself. If S 1 k S2 k Ø for all k, an k=0 (S1 k S2 k ) = Ø, there is a common asymptotic irection that is critical for {S 1 k } with respect to k S 2 k (or for {S2 k } with respect to k S 1 k ). Extens to any finite number of sequences {S j k }. Special Case: The intersection of set sequences efine by convex polynomial functions S j k = {x p j(x) γ j k, j = 1,..., r}, γj k 0, is nonempty, if all the k S j k an S1 k... Sr k are nonempty. (For example p j may be convex quaratic or biirectionally flat.)
23 EXISTENCE OF SOLUTIONS THEOREMS Convex Quaratic/Polynomial Problems: For j = 0, 1,..., r, let f j : R n R be polynomial convex functions. Then the problem minimize f 0 (x) subject to f j (x) 0, j = 1,..., r, has at least one optimal solution if an only if its optimal value is finite.
24 EXISTENCE OF SOLUTIONS THEOREMS Convex Quaratic/Polynomial Problems: For j = 0, 1,..., r, let f j : R n R be polynomial convex functions. Then the problem minimize f 0 (x) subject to f j (x) 0, j = 1,..., r, has at least one optimal solution if an only if its optimal value is finite. Extene FrankWolfe Theorem: Let f(x) = x Qx + c x where Q is symmetric, an let X be a close set whose asymptotic irections are retractive horizon irections with respect to X. If the minimal value of f over X is finite, there exists a minimum of f over X.
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