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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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7 results for “co-coercive”

Last-Iterate Guarantees for Learning in Co-coercive Gamescs.GT

Last-iterate guarantees for learning in co-coercive games under noisy feedback.

problem Learning in co-coercive games with noisy feedback.

method Vanilla stochastic gradient descent with a new noise model.

result Last-iterate bound of order

O(\log(t)/t^{1/3})

for co-coercive games.

co-coercive games vanilla SGD last-iterate guarantees noisy feedback non-vanishing noise

2026-04-21abs ↗pdf ↗

Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivitycs.LG

New convergence guarantees for SGDA and SCO under expected co-coercivity.

problem Solving smooth games with stochastic gradient descent-ascent and consensus optimization.

method Introducing expected co-coercivity and proving convergence guarantees for SGDA and SCO.

result Linear convergence of SGDA and SCO to a neighborhood of the solution with constant step-size, and convergence to the exact solution with stepsize-switching rules.

stochastic gradient descent-ascent consensus optimization smooth games expected co-coercivity convergence analysis

2021-06-30abs ↗pdf ↗

Variance-Reduced Fast Krasnoselkii-Mann Methods for Finite-Sum Root-Finding Problemsmath.OC

New method solves root-finding problems with faster convergence.

problem Finite-sum co-coercive equations

method Variance-reduced Krasnoselkii--Mann methods

result Achieves both

\mathcal{O}(1/k^2)

and

o(1/k^2)

convergence rates

root-finding finite-sum co-coercive Krasnoselkii--Mann variance reduction

2024-06-04abs ↗pdf ↗

Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problemsmath.OC

Develops new algorithms for solving root-finding problems in large-scale settings.

problem Solving nonlinear equations in large-scale settings.

method Randomized block-coordinate optimistic gradient algorithms.

result Achieves convergence rates of

\mathcal{O}(1/k)

and

\mathcal{O}(1/k^2)

for root-finding problems.

root-finding problems nonlinear equations large-scale settings optimistic gradient block-coordinate optimization

2023-01-08abs ↗pdf ↗

Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditionsmath.OC

Improved analysis of extragradient methods for structured VIPs.

problem Efficiently solving large-scale VIPs with weaker conditions.

method Single-call stochastic extragradient methods with expected residual condition.

result Convergence guarantees for quasi-strongly monotone and weak Minty VIPs.

extragradient VIPs non-monotone quasi-strongly monotone weak Minty

2023-02-27abs ↗pdf ↗

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networkscs.LG

This paper extends stability and generalization analysis of GD for multi-layer NNs.

problem Understanding the generalization of multi-layer neural networks trained by GD.

method Comprehensive stability and generalization analysis of GD for multi-layer NNs, focusing on two-layer and three-layer networks.

result Derives excess risk rates of

O(1/\sqrt{n})

for GD in two-layer and three-layer NNs under specific conditions.

neural networks gradient descent algorithmic stability generalization two-layer networks

2023-05-26abs ↗pdf ↗

A Class of Accelerated Fixed-Point-Based Methods with Delayed Inexact Oracles and Its Applicationsmath.OC

Develops accelerated fixed-point methods with delayed oracles for scientific computing.

problem Approximating fixed points of nonexpansive operators.

method Combines Nesterov's acceleration and KM iteration with delayed inexact oracles.

result Establishes improved convergence rates for fixed-point approximation.

fixed-point accelerated methods delayed oracles nonexpansive operators convergence rates

2025-12-15abs ↗pdf ↗