Research
On-device research index

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.

168,742 papers · 148 categories

Trend · papers per month

count% of corpus
0.3%0.5%0.8%0.1% · Dec 202319922001200920172026
7 results for “nonconvex-strongly-convex”
Accelerating Inexact HyperGradient Descent for Bilevel Optimizationmath.OC

New method finds stationary points in bilevel optimization problems.

problem Solving nonconvex-strongly-convex bilevel optimization problems.
method Restarted Accelerated HyperGradient Descent (RAHGD) method.
result Achieves best-known theoretical guarantees for finding stationary points in bilevel optimization.
bilevel optimizationhypergradient descentsecond-order stationary pointoracle complexitynonconvex-strongly-convex
2023-06-30abs ↗pdf ↗
Achieving O(ε−1.5){O}(ε^{-1.5}) Complexity in Hessian/Jacobian-free Stochastic Bilevel Optimizationmath.OC

Paper tackles Hessian/Jacobian-free stochastic bilevel optimization with O(ε−1.5){O}(ε^{-1.5}) complexity.

problem Nonconvex-strongly-convex bilevel optimization problem.
method FdeHBO optimizer with finite-difference Hessian/Jacobian-vector approximation and momentum.
result FdeHBO achieves O(ε−1.5){O}(ε^{-1.5}) iterations for εε-accurate stationary point.
bilevel optimizationHessian/Jacobian-freestochastic optimizationcomplexity analysisnonconvex-strongly-convex
2023-12-06abs ↗pdf ↗
Select without Fear: Almost All Mini-Batch Schedules Generalize Optimallycs.LG

New research shows many batch selection methods for training work just as well as full batch training.

problem Finding optimal batch selection methods for training.
method Analysis of mini-batch Gradient Descent (GD) and Stochastic GD (SGD) with various batch selection rules.
result All mini-batch schedules, including deterministic ones, generalize optimally for smooth Lipschitz-convex/nonconvex/strongly-convex loss functions.
mini-batch GDstochastic GDbatch selectiongeneralization errorconvex optimization
2023-05-03abs ↗pdf ↗
Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothnesscs.LG

New shuffling methods improve convergence without Lipschitz smoothness.

problem Lack of convergence guarantees for shuffling methods under non-Lipschitz conditions.
method Revisit shuffling methods, prove convergence under general bounded variance condition.
result Matched current best-known convergence rates without Lipschitz smoothness.
shuffling methodsconvergence ratesnon-Lipschitz smoothnessmachine learningempirical performance
2025-07-11abs ↗pdf ↗
Bilevel Optimization: Convergence Analysis and Enhanced Designcs.LG

The paper analyzes convergence rates of bilevel optimization algorithms and introduces a new stochastic algorithm.

problem Nonconvex-strongly-convex bilevel optimization problems in machine learning.
method Comprehensive convergence rate analysis for deterministic bilevel optimization using AID and ITD, and a novel stochastic algorithm stocBiO.
result Theoretical convergence rates for AID and ITD methods, and stocBiO's superior performance.
bilevel optimizationconvergence analysismeta-learninghyperparameter optimizationreinforcement learning
2020-10-15abs ↗pdf ↗
Accelerated Fully First-Order Methods for Bilevel and Minimax Optimizationmath.OC

Novel methods for accelerating optimization in complex bilevel and minimax problems.

problem Optimization challenges in bilevel and minimax problems, especially when strong convexity assumptions are not met.
method Accelerated fully first-order methods for Bilevel Optimization (BLO) and Minimax Optimization (NCSC).
result State-of-the-art complexity for finding approximate second-order stationary points in BLO and NCSC.
Bilevel OptimizationMinimax OptimizationAccelerated MethodsStrong ConvexityStationary Points
2024-05-01abs ↗pdf ↗