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 19
The Birkhoff Geometry of Manifold-Constrained Hyper-Connections: Two Channels, Vertex Viscosity, and Sinkhorn as a Retraction
Hyper-connections widen the residual stream of a Transformer to $n$ parallel streams.Faster high-accuracy multicommodity flow in dense graphs
We give a fast algorithm for solving min-cost $k$-commodity flow.Adversarial Training for Deep Hedging in Nonstationary Markets
Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent future market conditions.Trading with the STARS: Algorithm Design & Spectrum of Fundamental Limits for Trading with Storage
We study an online trading problem where a trader, given a sequence of i.i.d. prices drawn from a known distribution $F$ on $[0,1]$, must make irrevocable buy, sell, or hold decisions subject to…A Single-Loop, Constant-Batch First-Order Penalty Method for Stochastic Bilevel Optimization
Recent advances in penalty-based methods for stochastic bilevel optimization (SBO) have eliminated the need for second-order derivative oracles.Simplified Swarm Optimization for Surrogate-Assisted Reliability Design of Insulated-Gate Bipolar Transistor Power Modules Using an Open-Source Process Finite-Element Model
Process-induced warpage, ceramic stress and solder strain limit the reliability of insulated-gate bipolar transistor (IGBT) modules on direct-bonded copper (DBC) substrates.When the noncommutative AM-GM inequality holds
In this note, we prove that the noncommutative AM-GM inequality holds if $n\ge 2\lceil m/2 \rceil^2$.Near-Optimal Complexity of Finite-Sum Nonconvex-Strongly-Concave Minimax Optimization
We characterize, up to logarithmic factors, the minimax expected query complexity of finite-sum nonconvex-strongly-concave optimization under mean-squared averaged smoothness.Hamiltonian Metric Learning and Energy-Based Training: A Dissipative Geometric Framework for Optimization
Optimization in machine learning is usually expressed through iterative rules that update model parameters using information from the loss landscape.Optimal Oracle Complexity for Finite-Sum Monotone Inclusions
We present an oracle-optimal method for finite-sum monotone inclusions under mean-square Lipschitz continuity.Training Variational Quantum Algorithms Is NP-Hard, Even Locally
Variational quantum algorithms (VQAs) generally rely on classical optimization to train parameterized quantum circuits.On prediction from expert advice with more than five experts
We prove that no single rank ordered adversary strategy is globally optimal for the prediction with expert advice problem with six or more experts, in both the geometric stopping and finite time…Riemannian Shape Analysis of the Corpus Callosum in Kendall Space: Aging and Alzheimer's Disease
The corpus callosum (CC) is a major white-matter structure and a well-established marker of brain aging, but most studies quantify it using scalar summaries that discard its boundary geometry.Learning in Continuous Games from Pairwise Preference Feedback
We study learning in continuous games when players receive only pairwise preference feedback, revealing which of two actions is preferred but neither payoff values nor preference magnitudes.Taylor Representations for Model-Free RL in Networked MDPs
In Networked Markov Decision Processes, transition dynamics are often unknown and the state--action space grows rapidly with the number of agents.Distributed Algorithms for $α$-Potential Functions in General-Sum Games
We study the problem of computing the tightest \(α\)-potential approximation of a general-sum game over continuous action spaces, within a prescribed class of potential functions and when each…A Simple Algorithm Breaking the 1/3 Barrier for Approximate Nash Equilibria in Bimatrix Games
The PPAD-completeness of computing Nash equilibria in bimatrix games has driven extensive research into polynomial-time algorithms for $ε$-approximate Nash equilibria.Adaptive Bregman Alternating Projections for Feasible Gromov-Wasserstein Learning
The Gromov-Wasserstein (GW) problem compares structured distributions without requiring a shared feature space or known correspondences, but its nonconvex objective and coupled marginal constraints…Amortized Score-Hamiltonian Policy Iteration: A Grid-Free Scheme for Relaxed Stochastic Control Problems
We develop an amortized, grid-free implementation of continuous Langevin dynamics based policy-value iteration for entropy-regularized, infinite-horizon relaxed stochastic control problems.A KKL Observer Perspective on Reservoir Computing
Reservoir computing (RC) is a machine learning technique for data-driven modeling of dynamics for forecasting and control, primarily studied in computer science and physics literature with promising…