David Martínez-Rubio

activity

20202026

most citedGlobal Riemannian Acceleration in Hyperbolic and Spherical Spaces

4 citations · 7 across the 19 of their papers we have counts for

collaborators

Showing 2026Show all

7 papers · 1 filter

math.OC2026

Facial distance and diameter

Antoine Deza, David Martínez-Rubio, Javier Peña +1

The facial distance of a polytope, equivalently its pyramidal width, is a geometric condition measure that appears in the convergence bounds for Frank--Wolfe algorithms. Peña and W…

math.OC2026

Near-Optimal Acceleration for Smooth $ℓ_{p}$ / $ℓ_{q}$ Nondual Convex First-Order Oracle Optimization

David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán +1

We study the optimization of convex objectives with $(L, κ - 1)$ -Hölder-continuous gradients in $ℓ_{q}$ over $R B_{p}^{d}$ , $1 < κ \leq 2$ . (MG26) provides selectors with a movement bound fo…

math.OC2026

Stable Movement for Nondual Lipschitz Convex Optimization: Efficiency and Nearly Optimal Oracle Rates

David Martínez-Rubio, Cristóbal Guzmán

We study efficient algorithms for realizing the first-order oracle complexity of optimization of $G$ -Lipschitz convex functions with respect to the $ℓ_{q}$ -norm over an $\ell_{p…

math.OC2026

The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings

David Martínez-Rubio, Brian Bullins, Cristóbal Guzmán +1

We study first-order black-box convex optimization over an $ℓ_{p}$ -ball for objectives Lipschitz in the $ℓ_{q}$ -norm, solving in the affirmative the nonsmooth version of the COLT…

math.OC2026

Oracle Complexity of Stochastic Fixed-Point Equations with Nonexpansive Maps

Jelena Diakonikolas, Cristóbal Guzmán, David Martínez-Rubio

We study the oracle complexity of computing a point with small fixed-point residual $∥ T (x) - x ∥ \leq ε$ , for a general norm $∥ \cdot ∥$ and a self-map $T$ of a compact convex set.…

math.OC2026

Optimal Gradient-Norm Minimization in Non-Euclidean Hölder-Smooth Convex Optimization

Nico Pelleriti, Maryam Shiran, David Martínez-Rubio +2

Minimizing gradients of a convex function is an important problem across optimization and learning tasks. The gradient provides a directly computable certificate of approximate sta…

math.OC2026

Complexity of Classical Acceleration for $ℓ_{1}$ -Regularized PageRank

Kimon Fountoulakis, David Martínez-Rubio

We study the degree-weighted work required to compute $ℓ_{1}$ -regularized PageRank using the standard accelerated proximal-gradient method (FISTA). For non-accelerated methods (IS…

Facial distance and diameter

Near-Optimal Acceleration for Smooth ℓp​ / ℓq​ Nondual Convex First-Order Oracle Optimization

Stable Movement for Nondual Lipschitz Convex Optimization: Efficiency and Nearly Optimal Oracle Rates

The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings

Oracle Complexity of Stochastic Fixed-Point Equations with Nonexpansive Maps

Optimal Gradient-Norm Minimization in Non-Euclidean Hölder-Smooth Convex Optimization

Complexity of Classical Acceleration for ℓ1​-Regularized PageRank

Near-Optimal Acceleration for Smooth $ℓ_{p}$ / $ℓ_{q}$ Nondual Convex First-Order Oracle Optimization

Complexity of Classical Acceleration for $ℓ_{1}$ -Regularized PageRank