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 16
Optimal Algorithms for Nonsmooth Nonconvex Stochastic Optimization with Constraints
We present new algorithms for stochastic optimization problems in which the objective is neither smooth nor convex, subject to a convex constraint set.Randomized Momentum Transport with Cheap Predictors
Momentum methods such as STORM and second-order momentum track the gradient by adding a transport correction to momentum, at the cost of an extra oracle call per iteration.Adaptive Variance Reduction without Knowing Oracle Smoothness
Variance reduction improves the convergence rate of stochastic non-convex optimization under the average smoothness condition.Decentralized Bandit Learning of Markov Perfect Equilibria
We study decentralized bandit learning in discounted infinite-horizon, general-sum Markov games with continuous actions.STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization
TREGO, a trust-region Bayesian optimization (BO) method for expensive blackbox problems, has convergence guarantees, but only for exact function values.On the Depth Transfer of Neural Optimizers via L-Smoothness
Scaling deep learning models efficiently requires predictable performance transfer across compute regimes.Deep learning from a control vantage point
This article offers an overview of recent developments in deep learning from a control vantage point.A splitting proximal method in portfolio optimization with entropic value-at-risk
Motivated by a recent paper where a convex portfolio optimization problem formulated by means of the entropic value-at-risk is solved via a primal-dual interior-point method, we propose an…From Geometry to Generalization: Why Row Normalization Can Beat Adam and Muon
Different optimizers can fit the same training data while selecting classifiers with substantially different geometries, but whether this difference provably affects population performance remains…Deflating the Hessian: Rank-4 W4A4 Quantization for Multimodal Diffusion Transformers
In diffusion transformers, low-rank branches can mitigate 4-bit weight--activation (W4A4) post-training quantization (PTQ) loss by decomposing each weight into a low-bit residual and a…Rare Gate Disagreements Can Limit Plasticity: When Gradient Flow Mispredicts Finite-Batch SGD
Population gradient flow is a common tool for reasoning about how neural networks adapt, including after pretraining.A QPTAS for Stochastic Scheduling of Bernoulli Jobs
We study the classical problem of scheduling jobs with random processing times on $m$ identical machines to minimize the expected sum of completion times, for Bernoulli jobs: job $j$ takes time…Randomized Transport Maps for Model-Free Policy-Gradient Mean-Field Control
We develop a model-free policy gradient method for discrete-time mean-field control (MFC).Spectral Weight Decay: Inducing Low-Rank Structure in Neural Network Weights
Standard weight decay treats each weight matrix as a vector and ignores its spectral structure.Beyond QAOA: A Review of AI and Quantum Computing for Adaptive Combinatorial Optimization
Near-term quantum approaches to combinatorial optimization are limited by qubit counts, circuit fidelity, sampling cost, and the difficulty of encoding constraints, while machine learning is…Learning structured linear dynamical systems from missing observations
We consider the problem of learning structured linear dynamical systems over convex sets $\mathcal{K}$, where only a small subset of the observations are available at each time point.RobustLDS: Learning linear dynamical systems under adversarial corruptions
We consider the problem of learning linear dynamical systems under adversarial contamination from a single trajectory of length $T$.Multi-period Mean-Expectile Portfolio Optimization under Wasserstein Ambiguity: Reformulation, Degeneracy and the Role of the Ground Metric
Expectiles are the only law-invariant risk measures that are both coherent and elicitable.Escaping Degeneracy by Following Shadow Edges
The Simplex method is among the most widely used approaches for solving linear programs.Residual-Guided Global-Drive Design in Trapped-Ion Quantum Simulators
Trapped-ion quantum simulators are powerful platforms for exploring many-body physics, yet engineering of arbitrary interaction graphs remains a central challenge.