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 15
A Controlled Null Result for Per-Gate Riemannian Optimization in VQE
Parametrized gates in a variational quantum eigensolver (VQE) live on a product of unitary groups, which makes Riemannian optimization a natural candidate for improving training.Explicit Bounds on Hoffman Constants for Sparse Polyhedral Regularizers
Hoffman constants quantify the distance to the solution set of a polyhedral system in terms of its residual; they control error bounds, geometric conditioning, the convergence rate of first-order…Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning
We study federated learning from continuously arriving data: each client samples from its own distribution, these distributions drift over time, and the server must track the minimizer of the…Quantum ordinal optimization under a fixed comparison budget
Ordinal optimization selects designs that are good enough rather than optimal when evaluation is expensive and the budget is small.Entropy-Regularized Mean-Field Games with Average-Reward Criterion: Span-Seminorm Contractions and Model-Free Learning
We study stationary discrete-time mean-field games with finite state and action spaces under the average-reward criterion, with an entropy-regularized objective and population-dependent dynamics and…Two Linear Programs are Better than One
The extension complexity $\operatorname{xc}(P)$ of a polytope $P$ is the minimum number of facets of a polytope that projects onto it.Consumption adjustment with mean-reverting risk premia: A singular stochastic control approach
We study a two-sided singular stochastic control problem in which changes in consumption carry utility costs and the risk premium follows the mean-reverting Kim-Omberg model.Entanglement-Capability-Aware Finite-Bond Circuit Cutting for Distributed Quantum Computing
Distributed quantum computing distributes workloads that exceed a single processor's capacity across resource-constrained components.Ex Ante and Scenario-Conditional Value of Information in Dynamic Portfolio Choice
Portfolio decisions are often made under uncertainty about expected returns, and investors revise their beliefs as new information arrives.Mixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning
The average-reward setting for reinforcement learning is challenging because its Bellman operator is known to contract in span only when the underlying Markov chain is mixing.Training-Free Multi-Agent Cooperative Embodied Search from Uncertain Vision-Language Priors
Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations may be incomplete or incorrectly inferred by…RELAGRAM: Recursive Latent Graph Model for the Capacitated Vehicle Routing Problem
Latent recursion repeatedly processes internal representations through the same neural network.Non-Euclidean Gradient Descent Has Many Edges of Stability
Recent observations that deep learning models train at the edge of stability have renewed interest in understanding stability properties of classical optimization algorithms.When Solvers Get Moody: Quantifying and Predicting Performance Variability in Mixed Integer Programming
Mixed-integer programming (MIP) solvers are widely used, yet their performance can vary substantially even under seemingly neutral perturbations.AI-Mediation in Retail Markets: Revenue Effects and Assortment Optimization
AI shopping assistants increasingly influence which products consumers consider.Dynamic Matching with Abandonment: Optimal Guarantees via Static Priority Policies
We study stochastic dynamic matching with abandonment, a fundamental model in which agents of different types arrive over time, may abandon the market while waiting, and can be matched in pairs for…A Scalable Learned Iterative Solver for Nonlinear MPC via Temporal Decomposition
Nonlinear model predictive control (NMPC) can be applied to large nonlinear systems with complex objectives while enforcing constraints, but the cost of solving the underlying optimization problems…Lower Bounds for Randomized Linear-Oracle Optimization over Strongly Convex Sets
Recent results [arXiv:2602.04378, arXiv:2602.22608] have established worst-case objective-error lower bounds of order $1/T^2$ after $T$ linear minimization oracle (LMO) calls, matching the upper…Private and Distributionally Robust Federated Multiclass Classification
Fault diagnosis in asset-intensive industries requires multiclass models trained on noisy, sensitive data collected across geographically distributed client sites.Geometry-Aware Multi-Objective Optimization: Norm-Constrained Oracles with Entropic Weight Updates
Multiple-gradient descent is typically formulated in Euclidean geometry, whereas matrix-aware optimizers use directions induced by other norms.