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 28
Personalised versus Posted Pricing from Samples
Personalised pricing maximises expected revenue from a market but requires detailed information about individual customers.Resource-Adaptive Stochastic Gradient Descent for Online Linear Programming without Re-solving
The growth of large language model (LLM) inference and search services increases the scale of online linear programming problems, motivating computationally efficient algorithms.LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials
Control barrier functions (CBF) are a popular safety filter to ensure safety for nonlinear dynamical systems.Global Convergence of Third-Order Langevin Dynamics for Non-Convex Optimization via Simulated Annealing
We study global convergence guarantees of third-order Langevin dynamics for non-convex optimization via simulated annealing with fixed friction and decreasing noise.Sample-Based Prophet Inequalities for Random Walks
We study prophet inequalities for a random walk reward stopping problem with sample-based information.Optimal Investment and Consumption in Financial Markets with Integrated Variance Clocks
We study the infinite-horizon optimal investment and consumption problem in a general class of continuous financial markets, where uncertainty is driven by a continuous non-decreasing stochastic…Reliability Theory for AI Control
Reliability theory gives a mature language for layered systems, but its formal tools are not yet standard in frontier AI control.Polyak-Type Extragradient Methods for Monotone Root-Finding Problems
We study Polyak-type step-size selection for extragradient methods for solving deterministic and stochastic monotone root-finding problems.A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing
We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models.Silver Rate Is (Almost) Optimal for Gradient Descent: The Strongly Convex Case
We study gradient descent with predetermined nonnegative stepsizes on smooth strongly convex functions.Sharp Convergence of Wasserstein Gradient Flows for Spectrally Nonnegative Interaction Energies
We study the long-time behavior of Wasserstein gradient flows for interaction energies \[ \mathsf E[μ] = \frac12\iint_{M\times M}K(x,y)\,\mathrm dμ(x)\,\mathrm dμ(y) \] on a closed manifold $M$.Quantum-Enhanced Sampling of Schrödinger Bridges
We consider the dynamic Schrödinger bridge problem on a finite state space and exploit its Markov structure to decompose the problem into an endpoint coupling and a collection of conditional Markov…Active Portfolio Management in Concentrated Equity Markets
The equal-weighted portfolio is a passive, rule-based strategy that has historically been difficult to outperform, delivering higher returns than the capitalization-weighted "market" benchmark…Stochastic Inertial Krasnosel'skii-Mann Iteration Achieves Near-Optimal Sample Complexity
We analyze a simple stochastic inertial Krasnosel'skii--Mann (iKM) method for finding a fixed point of a nonexpansive operator in a real Hilbert space.The Critical Line Algorithm and the Constrained LASSO: One Curve, Two Literatures
Many statistical procedures compute an entire solution path rather than a single estimator.Complexities of Weak Proximal Oracle Methods for Composite Convex Optimization
We consider a standard convex composite optimization problem with either smooth or nonsmooth objective function, and under quadratic growth.Poisson Exchange Beyond Submodularity: Effective Approximation Algorithms for Offline and Online Subset Selection over Matroids
Over the past decade, a growing body of research has shown that $γ$-weak submodularity broadly arises in numerous subset selection tasks, including feature selection, neural network pruning, and…Reinforcement Learning in Operational Research: A Technical Review and Practical Roadmap
The growing demand for real-time, data-driven decision-making in complex and dynamic systems is placing increasing pressure on traditional Operational Research (OR) methodologies.Higher-Order Approximation of Exit Functionals in Sampling-Based Stochastic Model Predictive Control
Safety evaluation in sampling-based stochastic model predictive control often requires numerical estimation of exit functionals.Deep Reinforcement Learning on Item-Compatibility Graphs for One-Dimensional Bin Packing
The one-dimensional bin packing problem (1D-BPP) is a classical NP-hard combinatorial optimization problem with applications ranging from logistics and manufacturing to cloud resource management.