163 research outputs found
Quantitative Stability of Linear Infinite Inequality Systems under Block Perturbations with Applications to Convex Systems
The original motivation for this paper was to provide an efficient
quantitative analysis of convex infinite (or semi-infinite) inequality systems
whose decision variables run over general infinite-dimensional (resp.
finite-dimensional) Banach spaces and that are indexed by an arbitrary fixed
set . Parameter perturbations on the right-hand side of the inequalities are
required to be merely bounded, and thus the natural parameter space is
. Our basic strategy consists of linearizing the parameterized
convex system via splitting convex inequalities into linear ones by using the
Fenchel-Legendre conjugate. This approach yields that arbitrary bounded
right-hand side perturbations of the convex system turn on constant-by-blocks
perturbations in the linearized system. Based on advanced variational analysis,
we derive a precise formula for computing the exact Lipschitzian bound of the
feasible solution map of block-perturbed linear systems, which involves only
the system's data, and then show that this exact bound agrees with the
coderivative norm of the aforementioned mapping. In this way we extend to the
convex setting the results of [3] developed for arbitrary perturbations with no
block structure in the linear framework under the boundedness assumption on the
system's coefficients. The latter boundedness assumption is removed in this
paper when the decision space is reflexive. The last section provides the aimed
application to the convex case
Second-order subdifferential calculus with applications to tilt stability in optimization
The paper concerns the second-order generalized differentiation theory of
variational analysis and new applications of this theory to some problems of
constrained optimization in finitedimensional spaces. The main attention is
paid to the so-called (full and partial) second-order subdifferentials of
extended-real-valued functions, which are dual-type constructions generated by
coderivatives of frst-order subdifferential mappings. We develop an extended
second-order subdifferential calculus and analyze the basic second-order
qualification condition ensuring the fulfillment of the principal secondorder
chain rule for strongly and fully amenable compositions. The calculus results
obtained in this way and computing the second-order subdifferentials for
piecewise linear-quadratic functions and their major specifications are applied
then to the study of tilt stability of local minimizers for important classes
of problems in constrained optimization that include, in particular, problems
of nonlinear programming and certain classes of extended nonlinear programs
described in composite terms
Quantitative Stability and Optimality Conditions in Convex Semi-Infinite and Infinite Programming
This paper concerns parameterized convex infinite (or semi-infinite)
inequality systems whose decision variables run over general
infinite-dimensional Banach (resp. finite-dimensional) spaces and that are
indexed by an arbitrary fixed set T . Parameter perturbations on the right-hand
side of the inequalities are measurable and bounded, and thus the natural
parameter space is . Based on advanced variational analysis, we
derive a precise formula for computing the exact Lipschitzian bound of the
feasible solution map, which involves only the system data, and then show that
this exact bound agrees with the coderivative norm of the aforementioned
mapping. On one hand, in this way we extend to the convex setting the results
of [4] developed in the linear framework under the boundedness assumption on
the system coefficients. On the other hand, in the case when the decision space
is reflexive, we succeed to remove this boundedness assumption in the general
convex case, establishing therefore results new even for linear infinite and
semi-infinite systems. The last part of the paper provides verifiable necessary
optimality conditions for infinite and semi-infinite programs with convex
inequality constraints and general nonsmooth and nonconvex objectives. In this
way we extend the corresponding results of [5] obtained for programs with
linear infinite inequality constraints
Projectional Coderivatives and Calculus Rules
This paper is devoted to the study of a newly introduced tool, projectional
coderivatives and the corresponding calculus rules in finite dimensions. We
show that when the restricted set has some nice properties, more specifically,
is a smooth manifold, the projectional coderivative can be refined as a
fixed-point expression. We will also improve the generalized Mordukhovich
criterion to give a complete characterization of the relative Lipschitz-like
property under such a setting. Chain rules and sum rules are obtained to
facilitate the application of the tool to a wider range of problems
Variational Stability and Marginal Functions via Generalized Differentiation
Robust Lipschitzian properties of set-valued mappings and marginal functions play a crucial role in many aspects of variational analysis and its applications, especially for issues related to variational stability and optimizatiou. We develop an approach to variational stability based on generalized differentiation. The principal achievements of this paper include new results on coderivative calculus for set-valued mappings and singular subdifferentials of marginal functions in infinite dimensions with their extended applications to Lipschitzian stability. In this way we derive efficient conditions ensuring the preservation of Lipschitzian and related properties for set-valued mappings under various operations, with the exact bound/modulus estimates, as well as new sufficient conditions for the Lipschitz continuity of marginal functions
Generalized Differentiation of Parameter-Dependent Sets and Mappings
The paper concerns new aspects of generalized differentiation theory that plays a crucial role in many areas of modern variational analysis, optimization, and their applications. In contrast to the majority of previous developments, we focus here on generalized differentiation of parameter-dependent objects (sets, set-valued mappings, and nonsmooth functions), which naturally appear, e.g., in parametric optimization and related topics. The basic generalized differential constructions needed in this case are different for those known in parameter-independent settings, while they still enjoy comprehensive calculus rules developed in this paper
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