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 13
Per-State Signatures and Mean Time-to-Failure: An Algorithm for Systems With Simultaneous Failures
We present an algorithm that computes the structural signature and the mean time-to-failure (MTTF) of a monotone binary system at every one of its states, rather than only at the state in which all…Near-Optimal Parallel $M$-Matrix Scaling
In this paper we consider the problem of computing a positive diagonal scaling of a $d \times d$ symmetric $M$-matrix so that all row and column sums are $1$.BOLT: Multi-Bottleneck Bound-Guided Learning for Flexible Job-Shop Scheduling with Time Lags and Resource-Coupled Transport
Flexible job-shop scheduling becomes resource-coupled when a scarce vehicle fleet moves jobs between stations, because the schedule sets the transport waits.An improved randomized algorithm for finding sinks of Acyclic Unique Sink Orientations of the binary hypercube
Acyclic Unique Sink Orientations (AUSOs) of the binary hypercube form an elegant abstraction of various optimization problems, ranging from linear programs whose feasibility region is a…Deterministic Bounds on Accuracy and Stability of Metric-Space Regression and Classification
Data-driven approaches often face ambiguity in datasets due to sensor or target noise, insufficient information, and noise in the input data.Computing Fréchet Means on Unknown Submanifolds of Riemannian Manifolds
Empirical averages are fundamental statistical primitives.Hard Instances on Demand
The conclusions of an experimental study of algorithms are only as good as the instances they are run on.Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent
Random reshuffling stochastic gradient descent (RR-SGD) is widely used for finite-sum optimization in machine learning.On the Optimisation Landscape of Overparameterised Weighted MMD
We study the optimisation landscape of the maximum mean discrepancy (MMD) over weighted atomic measures.An Analytic 0.48-Approximation for Non-Monotone k-Submodular Maximization under a Matroid Constraint
We give a randomized polynomial-time $0.48$-approximation for maximizing a nonnegative non-monotone $k$-submodular function under a matroid constraint, for every $k\ge2$.Dimension-Optimal Dynamic Regret in Exp-Concave Online Learning
Tracking changing comparators is a central problem in online learning.When Does Momentum Help in Reinforcement Learning with Verifiable Rewards?
Momentum is a classical tool for accelerating convex optimization and a standard component of modern large language model training.Tracking a Small Set of Experts in Online Convex Optimization
For the fundamental problem of prediction from expert advice, Freund asked whether one could compete with a comparator that switches $k$ times among only $m$ experts.Learning Warm Starts for the Simplex Method from Measured Solver Cost
Learned warm starts for the simplex method build starting bases using predictions of which variables will be basic at optimality.Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning
Robust adaptive control is vital for keeping robots safe while actively reducing uncertainty in unfamiliar settings.Acceleration and its Limits in Gaussian Variational Inference
We study whether the classical momentum-based acceleration that improves the condition-number dependence of gradient descent from $κ$ to $\sqrtκ$ for strongly convex, smooth objectives extends to…On the Last-Iterate Convergence of Clipped Gradient Methods
In stochastic (strongly) convex optimization, establishing last-iterate convergence guarantees for first-order methods has recently garnered increasing attention, as this not only deepens the…When the implementation matters more than the algorithm: NSGA-II and other multi-objective optimizers in engineering design tools
Engineering design tools increasingly embed optimizers as ready-made components that users select by algorithm name, while operator settings and bound, integer and duplicate handling stay fixed…Convex-Analytic View of Query Selection for Safety Verification of Markov Decision Processes
Safety verification of a Markov decision process (MDP) asks whether the probabilities of reaching several failure states jointly lie in a convex safe region under every scheduler.Risk-Averse Inference-Time Diffusion Model Alignment with Finite-Sample Guarantees
Inference-time alignment steers diffusion models toward downstream objectives without costly fine-tuning, but existing methods typically optimize expected cost (or reward) without explicitly…