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 14
On integrability of local utility maximizing strategies
In stochastic control problems involving optimal portfolio choice, one often seeks an optimizer of some local criterion (dynamic programming value function) generated by some global criterion (e.g…Inclusion-wise extremal knapsack problems are (mostly) hard to approximate
We study two inclusion-wise extremal variants of the 0-1 Knapsack Problem (KP).A Practical Combinatorial Interior Point Method for Minimum-Cost Flow
In a recent breakthrough, Chen, Kyng, Liu, Peng, Probst Gutenberg, and Sachdeva (arXiv:2203.00671) designed the first almost-linear-time algorithm for minimum-cost flow.Last-Iterate Convergence of Clipped SGD with Linear Decay
In this paper, we study the last-iterate convergence of clipped stochastic gradient descent under heavy-tailed noise.Orientation Estimation of Cryo-EM Images for Molecules with Arbitrary Symmetry by Semidefinite Programming
This paper extends the non-unique games framework introduced in Bandeira et al. (2020) to address symmetric structures.A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus
We develop a rate-transfer method that converts finite-time Lyapunov estimates for an associated stochastic differential equation (SDE) into finite-time convergence bounds for stochastic gradient…Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs
Chance-constrained stochastic programs (CCSPs) require solutions that satisfy uncertain constraints with high probability.Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources
We study online task assignment with reusable resources.Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT
Because its binary optimization landscape contains exponentially many local optima, MAX-CUT is highly sensitive to initialization.Full-Order Safety for Nonlinear Reduced-Order MPC
Reduced-order models (ROMs) make model predictive control (MPC) of high-dimensional nonlinear systems tractable, but the resulting modeling error can lead to constraint violations on the real system.A Mean Field Model of Consumption Inertia and Keeping Up with the Joneses
Households compare their living standard with others' and dislike changing it.Online Submodular Welfare Maximization: Computation is not the Bottleneck
We revisit the online submodular welfare maximization problem (OSW), a vast generalization of numerous well-studied online allocation problems, including online bipartite matching, display ads and…Exploration strategies for Gaussian process bandit optimization with inference regret guarantees
Existing cumulative regret guarantees for Bayesian optimization (BO) do not necessarily characterize the quality of the algorithm's final output.Taming Nonconvexity with Epigraph Lifting
We consider inverse problems regularized by a trained neural network with \emph{convex activations} and \emph{unrestricted weights}.Asymptotic Risk of Learning-Rate Schedules for Least-Squares Regression with Discretely Distributed Features
We study the last iterate of stochastic gradient descent (SGD) on streaming least squares with discretely distributed features (Slock's model), which decouples the dynamics across eigendirections.Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements
We present a scalable method for identifying nonlinear dynamics from partial measurements, supporting physics-based, neural-network, and gray-box models.A Deterministic $0.401$-Approximation for Non-monotone Submodular Maximization under Matroid Constraints
Submodular maximization arises in a wide range of combinatorial optimization problems and has been extensively studied from the perspective of approximation algorithms.Calibrated $α$-Barycenters for Multiobjective $f$-Divergence Optimization
Matching one distribution to many targets can require repeated evaluations of every target.Feature Normalization under Finite Training Budgets
Standardizing a rare feature can help stochastic gradient descent learn its effect within a finite budget, but it also changes the noise cost of learning.GpFlash: Maximizing Speed and Minimizing Memory for Gaussian Process Training and Prediction
This paper presents GpFlash, a Gaussian process library that is focused on fast training and posterior evaluations, minimal memory usage, and scalability to high-dimensional problems.