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.
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Newest papers, page 35
Silver Rate Is (Almost) Optimal for Gradient Descent
We study how far gradient descent (GD) can be accelerated by predetermined stepsizes in smooth convex optimization.Support Discovery With Iteratively Reweighted Least Squares for Fixed-Charge Network Flow
The fixed-charge network flow problem (FCNFP) couples continuous flow allocation with discrete arc-activation decisions, making it a canonical but computationally challenging model for a variety of…A Block Tensor Train Burer-Monteiro Framework for Low-Rank Quantum State Tomography
Quantum state tomography is a fundamental technique for estimating the state of a quantum system from measured data and plays a crucial role in evaluating the performance of quantum devices.Exact-Form Regret for Gradient Descent, Mirror Descent and Follow-the-Regularized-Leader
Online gradient descent is usually studied through external regret, where the learner competes with fixed alternatives.Recovery Theory for Projected Power Iterations in Permutation Synchronization
We study the projected power method (PPM) for synchronizing \(n\) unknown permutations of \(m\) objects under a possibly sparse uniform corruption model.Oracle Complexity of Stochastic Fixed-Point Equations with Nonexpansive Maps
We study the oracle complexity of computing a point with small fixed-point residual $\|T(x)-x\| \leq ε$, for a general norm $\|\cdot\|$ and a self-map $T$ of a compact convex set.Nous: Learning and Certifying Memory Decisions Before Source Calibration
Agent memory systems update state decisions from reports whose reliability may be unknown.Constrained Online Learning with Noisy Constraint Values
We study constrained online convex optimization with adversarial constraints when constraint values and gradients are observed through unbiased noise.Input-to-State Stability Framework for Fully Distributed Primal-Dual Dynamics for Quadratic GNEPs Without Multiplier Consensus
Generalized Nash Equilibrium Problems (GNEPs) often arise in multi-agent engineering applications that require distributed algorithms.Mathematical Programming in Machine Learning and Artificial Intelligence: A Unified Taxonomy of Models and Applications
Mathematical programming provides a common language for many decisions embedded in modern machine-learning (ML) and artificial-intelligence (AI) systems: selecting retrieval context, routing tokens…MpSub: A Momentum $p$-Dimensional Subspace Trust-Region Method for Derivative-Free Fine-Tuning of Large Language Models
Full-parameter fine-tuning of large language models has substantial memory costs because backpropagation stores activations and gradients.A Theoretical Analysis of Generalization Dynamics in Neural Networks under Gradient Descent with Weight Decay
Understanding generalization remains a central challenge in machine learning because it requires jointly considering data, architecture, and training dynamics.Simple Dynamic Stock/Bond/Gold Portfolios
For more than four decades, the 60/40 stock/bond portfolio has served as a benchmark for delivering reasonable returns without excessive risk.Mini-Batch Risk-Averse Deep Q-Learning: A Robot Navigation Case Study
We study the control of Markov decision processes in which the quality of a policy is evaluated by a dynamic, time-consistent Markov risk measure rather than by an expected discounted cost.1.73-Optimal Echelon Stock (R,nQ) Policies in Two-Stage Stochastic Serial Systems
We consider a classical two-stage continuous-review serial inventory system with unit-sized Poisson demand of rate lambda, backlog cost rate p, lead times L_1, L_2, echelon holding-cost rates h_1…Computation and Applications of Euclidean and Normed Representations of Massive Data
This thesis investigates Euclidean-space and $\ell_p$-norm representations of different forms of data, with a focus on efficient algorithms for computing these representations in settings where the…Adaptive stabilization of a leaderless bearing-constrained formation with disturbances
In this paper, we consider the problem of regulating and maintaining a target formation characterized by a set of bidirectional bearing constraints under disturbances.Bi-HYCO: Bi-Objective Cooperative Learning for PDE Parameter Identification under Fragmented Observations
Physical and synthetic models may describe complementary aspects of the same PDE-governed system while receiving different, possibly fragmented, observations.Stochastic Nonconvex Bilevel Optimization: Improved Rates Without Rare-Visit Assumption
We investigate stochastic simple bilevel optimization with smooth and possibly nonconvex upper- and lower-level objectives.The Role of Gradient Modification in Heavy-Tailed Nonconvex Stochastic Min-Max Optimization
Stochastic min-max optimization has attracted increasing attention due to its applications in modern machine learning, while existing theoretical studies mainly rely on the bounded variance…