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 9
Affine-invariant Cubic Newton with Weak Learners
Modern gradient boosting algorithms are based on Newton's method, which excels empirically but lacks global convergence guarantees.Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network
High penetrations of distributed energy resources (DERs) and fast-varying electric vehicle (EV) charging intensify voltage and line-limit violations in distribution networks (DNs), making safe…V-Representability Beyond Singular Times in Time-Dependent Density Functional Theory: The Hubbard Dimer Case
We study the global $V$-representability problem in time-dependent density functional theory (TDDFT) for the two-particle Hubbard dimer model in the spin-singlet sector.EnerGNN: Learning Optimization-Compatible Energy Functions for Exact Constrained Combinatorial Inference
Learning-based approaches to combinatorial optimization often predict solutions directly or use predictions to restrict search, while expressive energy-based models typically rely on approximate…A Dynamic Inventory Rationing Model for Last-Mile Delivery of Medication in Liberia's Community Health Program
We study the dynamic allocation of scarce medications in Liberia's community health program.Empirically Calibrated Differential Games for Simulating Driver Interactions
Game-theoretic models can resolve vehicle interactions without hand-written priority rules, but whether they describe human drivers once calibrated has rarely been tested.On the Limits of Stochastic Push-Pull over Time-Varying Digraphs
Stochastic Push-Pull achieves linear speedup with respect to the network size $n$ on static strongly connected digraphs, but whether this speedup persists on time-varying networks remains unclear.Sharpness-aware Orthogonalized Momentum Algorithms
Adversarial sharpness penalty per iteration of a base optimizer, known as sharpness-aware minimization (SAM), is an accepted technique to improve generalization in deep learning.L0-Norm-Based CycleGAN for Unpaired Brain CT-to-MR Synthesis
Computed tomography (CT) and magnetic resonance (MR) imaging provide complementary information for the brain, but acquiring both modalities is not always possible.Contextual Decision Making: Predictive or Generative
Contextual decision making uses observed covariates to guide decisions under uncertainty.Multiplicative vs. Additive State Coupling in Learned Optimizer Schedules
We learn schedules for a continuous penalty relaxation of MAX-CUT by backpropagating through an unrolled classical solver.A Stationary Mean-Field Model for Carbon Market Stability Reserve
We formulate the design of a market stability reserve as a stationary mean-field singular control problem.Subgraph Isomorphism, Integer Linear Programming, and Lattices: A Linear Algebraic Framework
The subgraph isomorphism problem (SGI) asks a deceptively simple question: is a graph B found inside a parent graph A?Blind Random Search with Noisy Measurements: Almost Sure Convergence and Convergence Rates
Blind random search is a simple global optimization method that only requires the value of the loss function, without needing gradient information or an explicit loss model.When Are Nesterov Trajectories Eventually Locally Minimizing?
We revisit the variational characterization of Nesterov's celebrated method and study when Nesterov trajectories are eventually locally minimizing in a variational sense, a notion that we define…Deep ReLU Networks Can Fail the Best Approximation Property for Continuous Target Functions
We construct a continuous function on $[-1,1]^2$ for which the infimum of the $L^2$-approximation error over the class of ReLU networks with two hidden layers and architecture $(2,2,1,1)$ is not…Direct Inference on Admissible Sets (Diona)
We propose a new regression method named Direct Inference on Admissible Sets (Diona) that directly uses samples to estimate the state and its derivatives at a query point, along with their…Dispersive Tree Agreements for Multi-Robot Coordination
Multi-robot coordination of nonlinear systems is naturally formulated as joint-space optimal control subject to constraints over groups of robots.PnP*: A certifiable solver for Perspective-n-Point
Estimating a camera pose from 3D-2D correspondences - Perspective-n-Point - is one of the most fundamental problems in computer vision.Dynamics of Hidden Learning Structure
A learner's present predictions do not fully determine how it will learn next.