Valadier-like formulas for the supremum function I

We generalize and improve the original characterization given by Valadier [18, Theorem 1] of the subdifferential of the pointwise supremum of convex functions, involving the subdifferentials of the data functions at nearby points. We remove the continuity assumption made in that work and obtain a general formula for such a subdiferential. In particular, when the supremum is continuous at some point of its domain, but not necessarily at the reference point, we get a simpler version which gives rise to the Valadier formula. Our starting result is the characterization given in [11, Theorem 4], which uses the epsilon-subdifferential at the reference point.Comment: 27 page

Valadier-like formulas for the supremum function II: The compactly indexed case

We generalize and improve the original characterization given by Valadier [20, Theorem 1] of the subdifferential of the pointwise supremum of convex functions, involving the subdifferentials of the data functions at nearby points. We remove the continuity assumption made in that work and obtain a general formula for such a subdifferential. In particular, when the supremum is continuous at some point of its domain, but not necessarily at the reference point, we get a simpler version which gives rise to Valadier formula. Our starting result is the characterization given in [10, Theorem 4], which uses the epsilon-subdiferential at the reference point.Comment: 23 page

A non-convex relaxed version of minimax theorems

Given a subset $A\times B$ of a locally convex space $X\times Y$ (with $A$ compact) and a function $f:A\times B\rightarrow\overline{\mathbb{R}}$ such that $f(\cdot,y),$ $y\in B,$ are concave and upper semicontinuous, the minimax inequality $\max_{x\in A} \inf_{y\in B} f(x,y) \geq \inf_{y\in B} \sup_{x\in A_{0}} f(x,y)$ is shown to hold provided that $A_{0}$ be the set of $x\in A$ such that $f(x,\cdot)$ is proper, convex and lower semi-contiuous. Moreover, if in addition $A\times B\subset f^{-1}(\mathbb{R})$, then we can take as $A_{0}$ the set of $x\in A$ such that $f(x,\cdot)$ is convex. The relation to Moreau's biconjugate representation theorem is discussed, and some applications to\ convex duality are provided. Key words. Minimax theorem, Moreau theorem, conjugate function, convex optimization

Characterization of total ill-posedness in linear semi-infinite optimization

This paper deals with the stability of linear semi-infinite programming (LSIP, for short) problems. We characterize those LSIP problems from which we can obtain, under small perturbations in the data, different types of problems, namely, inconsistent, consistent unsolvable, and solvable problems. The problems of this class are highly unstable and, for this reason, we say that they are totally ill-posed. The characterization that we provide here is of geometrical nature, and it depends exclusively on the original data (i.e., on the coefficients of the nominal LSIP problem). Our results cover the case of linear programming problems, and they are mainly obtained via a new formula for the subdifferential mapping of the support function.Research supported by grants: SB2003-0344 form SEUI (MEC), Spain, MTM2005-08572-C03 (01) from MEC (Spain) and FEDER (E.U.), and ACOMP06/117 from Generalitat Valenciana (Spain)

Subdifferential Calculus Rules in Convex Analysis: A Unifying Approach Via Pointwise Supremum Functions

We provide a rule to calculate the subdifferential set of the pointwise supremum of an arbitrary family of convex functions defined on a real locally convex topological vector space. Our formula is given exclusively in terms of the data functions and does not require any assumption either on the index set on which the supremum is taken or on the involved functions. Some other calculus rules, namely chain rule formulas of standard type, are obtained from our main result via new and direct proofs.Research supported by grants MTM2005-08572-C03 (01) from MEC (Spain) and FEDER (E.U.), ACOMP06/117 and ACOMP/2007/247-292 from Generalitat Valenciana (Spain), and ID-PCE-379 (Romania)

Towards Supremum-Sum Subdifferential Calculus Free of Qualification Conditions

We give a formula for the subdifferential of the sum of two convex functions where one of them is the supremum of an arbitrary family of convex functions. This is carried out under a weak assumption expressing a natural relationship between the lower semicontinuous envelopes of the data functions in the domain of the sum function. We also provide a new rule for the subdifferential of the sum of two convex functions, which uses a strategy of augmenting the involved functions. The main feature of our analysis is that no continuity-type condition is required. Our approach allows us to unify, recover, and extend different results in the recent literature.Research of the first and the second authors is supported by CONICYT grants, Fondecyt 1150909 and 1151003, Basal PFB-03, and Basal FB003. Research of the second and third authors is supported by MINECO of Spain and FEDER of EU, grant MTM2014-59179-C2-1-P. Research of the third author is also supported by the Australian Research Council: Project DP160100854

Subdifferential of the supremum function: moving back and forth between continuous and non-continuous settings

In this paper we establish general formulas for the subdifferential of the pointwise supremum of convex functions, which cover and unify both the compact continuous and the non-compact non-continuous settings. From the non-continuous to the continuous setting, we proceed by a compactification-based approach which leads us to problems having compact index sets and upper semi-continuously indexed mappings, giving rise to new characterizations of the subdifferential of the supremum by means of upper semicontinuous regularized functions and an enlarged compact index set. In the opposite sense, we rewrite the subdifferential of these new regularized functions by using the original data, also leading us to new results on the subdifferential of the supremum. We give two applications in the last section, the first one concerning the nonconvex Fenchel duality, and the second one establishing Fritz-John and KKT conditions in convex semi-infinite programming.Research supported by CONICYT (Fondecyt 1190012 and 1190110), Proyecto/Grant PIA AFB-170001, MICIU of Spain and Universidad de Alicante (Grant Beatriz Galindo BEAGAL 18/00205), and Research Project PGC2018-097960-B-C21 from MICINN, Spain. The research of the third author is also supported by the Australian ARC - Discovery Projects DP 180100602

Moreau-Rockafellar-Type Formulas for the Subdifferential of the Supremum Function

We characterize the subdifferential of the supremum function of finitely and infinitely indexed families of convex functions. The main contribution of this paper consists of providing formulas for such a subdifferential under weak continuity assumptions. The resulting formulas are given in terms of the exact subdifferential of the data functions at the reference point, and not at nearby points as in [Valadier, C. R. Math. Acad. Sci. Paris, 268 (1969), pp. 39--42]. We also derive new Fritz John- and KKT-type optimality conditions for semi-infinite convex optimization, omitting the continuity/closedness assumptions in [Dinh et al., ESAIM Control Optim. Calc. Var., 13 (2007), pp. 580--597]. When the family of functions is finite, we use continuity conditions concerning only the active functions, and not all the data functions as in [Rockafellar, Proc. Lond. Math. Soc. (3), 39 (1979), pp. 331--355; Volle, Acta Math. Vietnam., 19 (1994), pp. 137--148].Research of the first and second authors is supported by CONICYT grants, Fondecyt 1150909 and 1151003, and Proyecto grant PIA AFB-170001. Research of the second and third authors is supported by MINECO of Spain and FEDER of EU, grant MTM2014-59179-C2-1-P. Research of the third author is also supported by the Australian Research Council, Project DP160100854