Applied Math · Mathematics & Statistics
Optimization & Control
The newest papers in Optimization & Control that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
754 papers, from 31 August 2026 to 5 November 2026: 698 arXiv preprints and 56 OpenAlex records.
Get the few papers worth your time in Optimization & Control every morning.
Follow Optimization & Control and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers
Last-Iterate Convergence of Stochastic Gradient Methods for Unbounded Problems
Last-iterate convergence guarantees for stochastic gradient methods have attracted significant attention as practical implementations typically return the last iterate instead of an average of the…From What Is Used to What Is Missing: Rethinking Dynamic Rank in LoRA
Parameter-efficient fine-tuning with LoRA is governed by rank, which controls parameter budget and locally accessible task updates.Truncated backpropagation through time induces a two-sided gradient turnpike property
Truncated backpropagation through time reduces the computational burden of training recurrent models by restricting gradient computations to shorter temporal contexts.Learning and Steering in Incomplete-Information Games
In many modern multi-agent systems, a mediator seeks to influence strategic decision dynamics using only outcome-level feedback and without knowing agents' utilities.A Bilevel Neurodynamic Approach for Distributed SCAD-Regularized Sparse Least Squares
This paper proposes a bilevel particle swarm optimization (PSO)-neurodynamic method for solving the distributed sparse least-squares problem regularized by the smoothly clipped absolute deviation…Projection-Free Gradient Tracking Neurodynamic for Distributed Nonnegative Sparse Optimization
This paper proposes a continuous-time, projection-free neurodynamic system based on gradient tracking for distributed nonnegative sparse optimization.Randomized Parallel Complexity of Weakly Convex Optimization
We study the minimum number of query rounds needed to find approximate stationary points of nonsmooth weakly convex functions, measured by the gradient of the Moreau envelope.Randomized and Oracle-Based Feasible Directions Methods for Nonsmooth Nonconvex Constrained Optimization
Minimizing nonsmooth nonconvex objectives over general convex sets using no derivatives of the objective, together with structural access to the constraint set, poses a fundamental geometric…Newton Steps are Faster for Linear Complementarity Problems
Linear Complementarity Problems (LCPs) with sufficient matrices form a well-studied class of problems, dating back from the foundational works of Cottle and Dantzig (1968).XM4D: 4D Reconstruction through Certifiable Geometric Optimization
Accurate 4D reconstruction remains challenging: jointly estimating camera motion, scene geometry, and non-rigid deformation entails highly nonconvex optimization, making global optimality difficult…Reversible Dynamics, Asymmetric Actions: Parking as a Trajectory-Level Affordance
Drivers are often told to back into a tight parking space, not drive in nose first.Quantifying the remaining search space in analogue optimisation
Benchmarking analogue optimisers requires certifying not only how close a returned energy is to the optimum, but also how many better configurations may remain.All minimum cuts of symmetric submodular functions
We compute Cunningham's canonical decomposition of a symmetric submodular function on $n$ elements in $O(n^3)$ time.Dynamic Optimistic Constrained OCO with Memory via Delay Equivalence
Online Convex Optimization with memory and time-varying constraints (COCO-M) is a recently-introduced framework that captures the dynamics of stateful online learning and non-stochastic control with…A Theoretical Analysis of a Bilevel Optimization Formulation for Combined Local-Global Optical Flow Models
This paper analyzes a bilevel formulation for learning the regularization parameter of combined local-global (CLG) optical flow models.Adversarial Training with Large Step Sizes: Implicit Bias, Optimization, and Sharpness
Adversarial Training (AT) is an important method for improving model robustness.Near-Optimal Complexity of Finding Approximate Local Minima of Nonconvex Finite Sums
We study the component-oracle complexity of finding approximate local minima of nonconvex finite sums under mean-squared averaged smoothness with parameter $L$ and a $ρ$-Lipschitz average Hessian.Pathwise-Budgeted LLM Routing: Online Allocation with K-Bit Tail-Cost Learning
Firms using large language models under fixed budgets must decide which model to call and whether to retry a failed task.On Convergence of Gradient Temporal-Difference Learning with Regularized Corrections and Eligibility Traces
Temporal-difference (TD) learning underlies many prediction and control algorithms, but can diverge when combined with off-policy learning, bootstrapping, and function approximation.Attraction-Ranking Mismatch in Out-of-Distribution Detection: Theory and Evidence from Hamiltonian Feature Dynamics
Attracting features toward learned in-distribution (ID) regions is an intuitive strategy for out-of-distribution (OOD) detection, but does such attraction improve detection?