Applied Math · Mathematics & Statistics
Optimization & Control
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754 papers, from 31 August 2026 to 5 November 2026: 698 arXiv preprints and 56 OpenAlex records.
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Newest papers, page 23
Fast Regularized Policy Mirror Descent with One-Step TD Updates
Policy mirror descent (PMD) enjoys fast convergence in regularized Markov decision processes (MDPs), but existing guarantees often rely on exact or increasingly accurate policy evaluation.Super-Quadratic Quantum Speedups for Combinatorial Optimization via Tilted Walks
We introduce quantum tilted walks, a quantum algorithmic framework for solving exact combinatorial optimization problems.Scalable Cox Regression via Grouped Risk Sets and Sharper LogSumExp Rates
Motivated by the computational challenges of large-scale Cox regression, we study stochastic minimization of LogSumExp objectives over large sets.Policy Iteration Is Not Strongly Polynomial for Deterministic Markov Decision Processes: The Price of Algorithmic Anarchy
We establish an exponential iteration lower bound in the number of states for Howard's policy iteration on deterministic discounted Markov decision processes, with at most two actions per state.Exact information accounting for SGD methods
As an alternative to the standard geometric analyses, we give an exact, information-theoretic analysis of stochastic gradient descent (SGD) and its variants.Admissibility-Preserving Control for Multi-Input Systems with Joint Capacity Constraints
This paper addresses the control of multi-input strict-feedback nonlinear systems subject to a joint capacity constraint, in which the admissible input set is a coupled subset of the individual…Q-MINO: A Minimal-Norm Method for Quantization-Aware Training
The Straight-Through Estimator (STE) is a widely used heuristic for Quantization-Aware Training (QAT), but its surrogate gradients can exhibit substantial mismatch with the underlying quantized…Parameter-Free Interval-Dynamic Regret under Heavy-Tailed Noise
We study online convex optimization with one unbiased stochastic subgradient per round and an unknown finite conditional $p$th noise moment, $1<p\le2$.BRIDGE: Bilevel Retrieval-Credit-Aware Agentic Reinforcement Learning
Agentic reinforcement learning (ARL) with verifiable rewards improves the ability of large language models (LLMs) to tackle knowledge-intensive tasks by learning to interleave search and reasoning.Second-Moment Stochastic Approximation Methods
Classical stochastic approximation methods rely on estimators of the first moment (mean) of a random regression function.Byzantine-Robust Federated Representation Learning
We study federated learning (FL) with adversarial clients, where the goal is to minimize the average loss of the honest (non-adversarial) clients without knowing their identity.Stochastic Heavy Ball with Polyak Step Size and Armijo Line Search: A General Convergence Analysis
Polyak step size (PS) and Armijo line search (ALS) have received increasing attention in stochastic optimization, with encouraging empirical performance and theoretical guarantees.Fine-Tuning on Self-Generated and Reward-Weighted Data: Learning Dynamics, Convergence Rates, and Benefits of Off-Policyness
We study the learning dynamics of fine-tuning a policy model on self-generated and reward-weighted data, with particular focus on a generalized version of REINFORCE -- referred to as RE(S) -- that…Hybrid Joint-Selective Optimization: Reduced-Space Levenberg-Marquardt Refinement of Low-Dimensional Parameters of Interest
This paper introduces a hybrid joint-selective optimization (HJSO) framework for large-scale numerical problems in which a small subset of trainable quantities is of primary interest.Teaching LLMs to Generate Challenging MILP Instances via Solver Feedback
Generating optimization instances that are both feasible and computationally challenging is crucial for benchmarking solvers and training learning-based optimization algorithms.A stochastic subgradient method with optimal failure exponent
Fix a target accuracy $\varepsilon$, a gradient-noise level $s$, and a horizon $N$.Strict-Saddle Landscapes and Multi-Rank Geometry in Low-Tubal-Rank Tensor Sensing
We study the optimization landscape of low-tubal-rank tensor sensing through a balanced factorization.Mutual Information Constrained Chernoff Bottleneck
The classical information bottleneck (IB) measures the relevance of a representation $U$ of $X$ to a target $Y$ by $I(U;Y)$, which does not directly characterize the error of downstream decisions.Dr. OPD: Learning What to Follow for Optimal On-Policy Distillation of Large Language Models
On-policy distillation (OPD) trains a student on its own generated responses using dense, token-level supervision from a stronger teacher.The finite-horizon five-expert prediction problem
We give an explicit solution to the five expert prediction with expert advice partial differential equation (PDE) in the finite-time horizon setting.