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 25
Neural networks for spectral optimization
Given a functional dependent on the spectrum of a differential operator, we address the problem of finding a domain which optimizes this functional.Tensor-Train Compressed Separable PINNs: A Curvature-Aware Optimization Framework for Parametric PDEs in High Dimensions
In this work, we develop a second-order optimization framework for physics-informed neural networks (PINNs) applied to high-dimensional parametric partial differential equations (PDEs).Can Representation Learning Decouple from Loss Minimization? Polar Updates Have an Answer
Does representation learning stop when the training loss stops improving?Plug-and-Play Methods Provably Converge Even with Improperly Trained Denoisers: Convergence by Architectural Design
Plug-and-Play (PnP) methods replace proximal operators with learned denoisers, which produce state-of-the-art reconstruction quality, but sacrifice the variational interpretation and convergence…ILP-BO: Integer Linear Programming-Based Black-Box Optimization
Black-box Optimization (BO) is a powerful framework for optimizing expensive objective functions or unknown functions with a limited number of evaluations.Convergence of Practical Muon
Muon is emerging as a promising alternative to AdamW for large-scale neural network training, yet theoretical understanding of its practical implementation remains incomplete, as existing analyses…Sharp Critical Minimax Laws and No-Learning Thresholds in Continuous-Time Adaptive Control
We study episodic continuous-time control with an unknown vector control gain, scalar state, quadratic action cost, and smooth convex terminal cost.Notes on Generative Modeling for Feedback Control and Planning
In these notes, we view control as a dynamically constrained sampling problem on the state-space of a control system.The Price of Locality: Why Forward-Forward Underperforms Backpropagation?
The Forward-Forward Algorithm (FFA) replaces backpropagation (BP) with layer-wise local contrastive objectives, eliminating the backward pass and the need to retain intermediate activations, yet…Local LMO is Secretly a Projection Method!
The local linear minimization oracle (Ferris and Zavriev, 1996; arXiv:2605.08850), or Local LMO, solves constrained convex problems without having to compute a projection: it minimizes a linear…Recovering Lower-Dimensional Semialgebraic Support of a Measure from its Moments
Recovering probability measures from their moments has numerous applications, esp. in connection with the method of moments in statistics and optimization.Geometric Identification in Predict-Then-Optimize Learning
Decision-focused surrogates can recover downstream decisions without identifying the quotient report.The cost of useful natural gradient updates
What information is needed to turn a natural-gradient direction into a useful finite update?lapanda: A Matrix-Free Differentiable Solver for Nonconvex Constrained Optimization Layers
Differentiable optimization brings the structural guarantees of mathematical optimization to network pipelines, allowing them to be trained end-to-end.Annealed Sinkhorn with Momentum: Certified Unregularized Optimal Transport in Linear Memory
We characterize Bregman Douglas-Rachford splitting (BDRS) for unregularized discrete optimal transport and develop an anytime primal-dual certificate in linear memory.dOPT: Differentiating Conic Optimization via Geometric Reduction
Optimization layers enable the incorporation of structured constraints and decision problems into learning systems.DCEmbed: Scalable Optimization over Neural Surrogates
Neural surrogates can accelerate large-scale optimization by replacing expensive or intractable model components with efficient learned approximations, but solving the resulting embedded problems…Structure-Adaptive Tree Field Integrators
We present a new class of near-linear algorithms for efficiently integrating general tensor fields defined on trees with distance dependent kernels, the Structure-Adaptive Tree Field Integrators…Convergent Plug-and-Play Image Restoration with Annealed Noise Levels
Plug-and-Play (PnP) methods solve imaging inverse problems by incorporating deep denoisers into iterative optimization algorithms.LocalProp: Neuro-Localized Memory-Efficient Backpropagation
The current deep learning training paradigm employs end-to-end backpropagation, regardless of the training stage, i.e. pre-training or fine-tuning.