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 32
Learning to Solve Stochastic Controls with Unknown Drifts and Running Rewards: Theory, Algorithms and Convergence
We study continuous-time and possibly high-dimensional stochastic control problems where drift coefficients and running reward functions are unknown.Matrix Spencer: Eight Standard Deviations Suffice and an Almost-Linear Time Algorithm for Dense Input
The Matrix Spencer conjecture asserts that for all symmetric matrices $A_1,\ldots,A_n\in\mathbb{R}^{n\times n}$ with $\|A_i\|\le1$ there are signs $\varepsilon_1,\ldots,\varepsilon_n\in\{-1,1\}$…Eigenvalue-Decomposition Cost Denoising as an Alternative to Predict-then-Optimize for Shortest-Path Problems
Predict-then-optimize methods such as Smart "Predict, then Optimize" (SPO+) of Elmachtoub and Grigas (2022) learn a mapping from contextual features to unknown edge costs and then solve the induced…Improving the Last-Iterate Guarantees of Anytime Algorithms for Stochastic Monotone Variational Inequalities
We analyze a stochastic algorithm with Halpern anchoring for constrained convex-concave problems and monotone variational inequalities.Projection-Free Multi-level Algorithms for Stochastic Constrained Compositional Optimization
This paper studies projection-free algorithms for stochastic constrained multi-level compositional optimization.Volumetric Harmonic Field Navigation for Quadrotors
Quadrotor navigation in cluttered 3-D environments requires global guidance while local motion remains subject to collision and motion limits.Solving Finite-sum Coupled Compositional Optimization via Multi-block-Single-probe Estimator
Traditional variance reduction methods (e.g., SPIDER, SARAH, STORM) have been extensively investigated for improving the convergence rates of stochastic optimization.When Should a World Model Move? Loss-Conditioned State Execution
We introduce loss-conditioned state execution, a model-agnostic method that decides whether to execute a world model's fixed feasible proposal or retain the current state.Strategic Index Reconstitution: Differential Games, Closed-Loop Equilibria and Mean-Field Dynamics
We study strategic trading around index reconstitution in a continuous-time, multiasset game with transient cross-asset price impact and heterogeneous beliefs about future index membership.Bellman Policy Optimization
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models (LLMs).The fixed-point bundle method over product-of-simplex domains arising from game equilibria
This paper extends the fixed-point bundle framework for finite-dimensional variational inequalities (VIs) from the simplex domain to the product-of-simplex domain, which is directly applicable to…Robust low-rank tensor completion via factorized weighted tensor schatten-p norm minimization
Low-rank tensor factorization provides a flexible framework for completing multidimensional data from incomplete and corrupted observations.Evaluation of optimisation and Bayesian inference methods for reaction rates in atmospheric chemical mechanisms
Constraining reaction rate coefficients is a central challenge in the development of explicit atmospheric chemical mechanisms, particularly for autoxidation systems where many reaction pathways are…Privacy Preserving Gossip Learning
We propose a decentralized privacy-preserving learning algorithm in which each agent holds a single private sample and a shared model.Curvature-Independent Regret Bounds for Distributed Online Optimization on Hadamard Manifolds
This work addresses decentralized online Riemannian optimization on Hadamard manifolds.Nonsmooth Optimization via Orthogonalized Momentum
Modern real application problems involve matrix-valued parameters, yet conventional optimizers treat them as vectors, thereby motivating matrix-aware methods that exploit input-output geometry, such…On the Equivalence of Stochastic Control and Path Space Formulations for Schrödinger Bridges over Compact Connected Lie Groups
We establish the equivalence between the stochastic optimal control and path space formulations of the Schrödinger bridge problem (SBP) for the kinematic equation on a compact connected Lie group.Benchmarking Optimizers to Solve Inverse Problems with Differentiable Physics Simulators
Solving inverse problems with differentiable physics simulators holds the potential to revolutionize scientific discovery and engineering design, as it enjoys both the strict physical correctness…Generation of Custom Solvers in Rust for Convex Optimization
We introduce cvxgenrust, an open-source tool for generating custom Rust code that solves families of parameterized convex optimization problems modeled in CVXPY. cvxgenrust canonicalizes a problem…CyclOT: Learning Quadratic Optimal Transport Maps via Synchronized Forward-Backward Interpolants
We study the recovery of forward and reverse quadratic optimal-transport maps from unpaired samples in high dimensions.