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J. X. Cruz Neto

Publications and source records attributed to J. X. Cruz Neto.

11 recordsLinked to original sources

Busemann-coupling subdifferentials and Fenchel biconjugation on Hadamard manifolds

We introduce an intrinsic subdifferential on Hadamard manifolds induced by a base-point-dependent Busemann coupling, motivated by the scarcity of nonconstant affine functions on non-Euclidean Hadamard manifolds. For proper functions, it characterizes exactly the primal-dual equality pairs in the Fenchel-Young inequality of Bento, Cruz Neto, and Melo (\emph{Appl. Math. Optim.} 88:83, 2023). A dual-point--tangent-vector bijection yields Fermat's rule and isometry covariance. We characterize everywhere nonemptiness for finite-valued functions by pointwise-attained \(H_p\)-envelope representations, encompassing the radial models with explicit subdifferentials and the nonsmooth distance from the base point. In hyperbolic space, base-point rigidity shows that, for functions differentiable at the base point, nonemptiness is equivalent to global minimality and vanishing of the corresponding Fenchel-Young gap. Two geodesically convex functions on the Poincaré disk prove strict noninclusions between our construction and fixed-direction Busemann subdifferentials, although both recover the classical Euclidean subdifferential. This separation is explained by coupling orientation. In constant negative curvature, a strictly increasing scalar profile describes the asymmetry and defines an intrinsic measure of nonlinearity. We compute this measure at arbitrary radius and derive a lower bound and two-sided estimates for the biconjugation gap; at unit radius, the exact curvature-dependent bound is attained by deviations of both signs.

math.DG

Subgradient Methods on Manifolds with Lower Bounded Curvature

The subgradient method is a classical and foundational approach in non-smooth convex optimization; its simplicity, robustness, and role as a conceptual and algorithmic starting point have made it the backbone of many significant optimization algorithms. Motivated by classical Euclidean results and recent advances in first-order Riemannian optimization, we study the convergence of the subgradient method on Hadamard manifolds with lower bounded curvature. Assuming a nonempty solution set and employing a corresponding non-summable diminishing step-size condition, we establish convergence of the generated sequence $\{x^k\}$ to a minimizer whenever at least one of the following holds: (a) the sequence $\{x^k\}$ is bounded; (b) the solution set $S$ is bounded; or (c) the step-sizes are square-summable ($\sum_{k=1}^{\infty}λ_k^2<\infty$). Additionally, we prove that if $\operatorname{int}(S)\neq\emptyset$, the method achieves finite termination. Our main contribution provides a Riemannian counterpart to Shepilov's Euclidean analysis [Cybernetics, 12 (1976), pp. 544-548], thus complementing existing literature on convex minimization over manifolds with lower bounded curvature.

math.OC

Regularized Multiobjective Optimization with Directionally Lipschitzian Data

The paper is devoted to the study of regularized versions of multiobjective optimization problems described by directionally Lipschitzian functions. Such regularizations appear in proximal-type algorithms of multiobjective optimization, various models of machine learning, medical physics, etc. We investigate and illustrate several useful properties of directionally Lipschitzian functions, which distinguish them from locally Lipschitzian ones. By using advanced tools of variational analysis and generalized differentiation revolving around the limiting/Mordukhovich subdifferential, we derive necessary conditions for Pareto optimality in regularized multiobjective problems.

math.OC

A Refined Proximal Algorithm for Nonconvex Multiobjective Optimization in Hilbert Spaces

This paper is devoted to general nonconvex problems of multiobjective optimization in Hilbert spaces. Based on Mordukhovich's limiting subgradients, we define a new notion of Pareto critical points for such problems, establish necessary optimality conditions for them, and then employ these conditions to develop a refined version of the vectorial proximal point algorithm with providing its detailed convergence analysis. The obtained results largely extend those initiated by Bonnel, Iusem and Svaiter \cite{Bonnel2005} for convex vector optimization problems and by Bento et al. \cite{Bento2018} for nonconvex finite-dimensional problems in terms of Clarke's generalized gradients.

math.OC

Elements of Convex Geometry in Hadamard Manifolds with Application to Equilibrium Problems

In this paper, is introduced a new proposal of resolvent for equilibrium problems in terms of the Busemann's function. A great advantage of this new proposal is that, in addition to be a natural extension of the proposal in the linear setting by Combettes and Hirstoaga in [20], the new term that performs regularization is a convex function in general Hadamard manifolds, being a first step to fully answer to the problem posed by Cruz Neto et al. in [21, Section 5]. During our study, some elements of convex analysis are explored in the context of Hadamard manifolds, which are interesting on their own. In particular, we introduce a new definition of convex combination (now commutative) of any finite collection of points and present the realization of an associated Jensen-type inequality.

math.OC

Convexity and some geometric properties

The main goal of this paper is to present results of existence and non-existence of convex functions on Riemannian manifolds and, in the case of the existence, we associate such functions to the geometry of the manifold. Precisely, we prove that the conservativity of the geodesic flow on a Rieman- nain manifold with infinite volume is an obstruction to the existence of convex functions. Next, we present a geometric condition that ensures the existence of (strictly) convex functions on a particular class of complete non-compact man- ifolds, and, we use this fact to construct a manifold whose sectional curvature assumes any real value greater than a negative constant and admits a strictly convex function. In the last result we relate the geometry of a Riemannian manifold of positive sectional curvature with the set of minimum points of a convex function defined on the manifold.

math.DG

Proximal algorithms with Bregman distances for bilevel equilibrium problems with application to the problem of "how routines form and change" in Economics and Management Sciences

In this paper we present the bilevel equilibrium problem under conditions of pseudomonotonicity. Using Bregman distances on Hadamard manifolds we propose a framework for to analyse the convergence of a proximal point algorithm to solve this bilevel equilibrium problem. As an application, we consider the problem of "how routines form and change" which is crucial for the dynamics of organizations in Economics and Management Sciences.

math.OC

A proximal point algorithm with generalized proximal distances to BEPs

We consider a bilevel problem involving two monotone equilibrium bifunctions and we show that this problem can be solved by a proximal point method with generalized proximal distances. We propose a framework for the convergence analysis of the sequences generated by the algorithm. This class of problems is very interesting because it covers mathematical programs and optimization problems under equilibrium constraints. As an application, we consider the problem of the stability and change dynamics of task's allocation in a hierarchical organization.

math.OC

Behavioral Traps and the Equilibrium Problem on Hadamard Manifolds

In this paper we present a sufficient condition for the existence of a solution for an equilibrium problem on an Hadamard manifold and under suitable assumptions on the sectional curvature, we propose a framework for the convergence analysis of a proximal point algorithm to solve this equilibrium problem in finite time. Finally we offer an application to personal equilibrum problems as behavioral traps problems, using a recent "variational rationality" approach of human behavior.

math.OC

The self regulation problem as an inexact steepest descent method for multicriteria optimization

In this paper, we study an inexact steepest descent method, with Armijo's rule, for multicriteria optimization. The sequence generated by the method is guaranteed to be well-defined. Assuming quasi-convexity of the multicriteria function we prove full convergence of the sequence to a critical Pareto point. As an application, this paper offers a model of self regulation in Psychology, using a recent variational rationality approach.

math.OC