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I. D. L. Melo

Publications and source records attributed to I. D. L. Melo.

3 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↗

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↗