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 8
Neural Compression of Long ADMM Trajectory for Multiparametric Quadratic Program
Solving large-scale multiparametric quadratic programs (mpQPs) in real time often requires thousands of iterative optimization updates, creating a major computational bottleneck in model predictive…Rank-one Factored Gradient Descent without Restricted Isometry: Application to Quantum Pure State Tomography on Multi-Qubits
Factored gradient descent (FGD) is a scalable nonconvex method for low-rank positive semidefinite matrix recovery, in which the rank and positivity constraints are satisfied automatically by the…Reinforcement learning for constrained stochastic linear--quadratic control in continuous time
We study continuous-time stochastic linear--quadratic control with a scalar state and nonnegative control constraints.Global $\mathcal{O}\left(k^{-2}\right)$ Convergence of Multilevel Gradient-Regularized Newton Methods
We develop and analyze multilevel gradient-regularized Newton methods for unconstrained convex optimization with Lipschitz-continuous Hessians.Understanding Practical Optimization in Machine Learning
Modern machine learning relies on practical optimization methods that often differ from the algorithms studied in classical analyses: they train submodels, clip or normalize gradients, use…Autonomous Structural Discovery Of Bioprocess Dynamics And Kinetics Via LLM-Driven Agents
High-fidelity digital twins in continuous biomanufacturing are often bottlenecked by incomplete kinetic models, while standard data-driven algorithms (e.g., SINDy, PINNs) lack interpretability or…Benchmarking quantum-annealing-based approaches for factory layout planning
The earlystage of factory layout planning has a direct effect on the operating costs, efficiency, and productivity of manufacturing systems.Constraint Learning by Optimizing over Trained Neural Networks: A Pragmatic Tutorial
Constraint learning is an emerging paradigm of modeling optimization problems with the help of machine learning.SCAFFOLD under Hessian Similarity: Sharp Convergence Rates
One of the main challenges of federated learning is heterogeneous data split among the clients, leading to biased local updates called client drift.A Graph-informed Kriging model for sample-efficient Bayesian optimization of wind farm layouts
Wind farm layout optimization becomes increasingly difficult as turbine count grows, especially when high-fidelity aerodynamic models are used.Minimax-Optimal Small-Loss Bounds for Online Linear Optimisation on $\ell_p$ Balls
We study full-information online linear optimisation on the unit $\ell_p$ ball in $\mathbb{R}^d$, for fixed $2\le p<\infty$ and losses of dual norm at most one.AHEAD: Adaptive Neural Acceleration for Large-Scale Linear Programming
Large-scale linear programs with millions of variables and constraints strain time and memory budgets.Balanced Active Subspaces for Optimal Control via Trajectory Optimization
The curse of dimensionality associated with solving the Hamilton-Jacobi-Bellman equation for the value function remains a key obstacle for real-time optimal feedback control of high-dimensional…A Nonconvex Framework for Joint Sparse Sample Weighting and Parameter Estimation
We propose a joint sparse sample weighting and parameter estimation (SWAPE) framework that jointly optimizes model parameters and sample weights to train a model while simultaneously assigning an…Optimal ratcheting dividend policies with resets
We study an optimal dividend policy problem under ratcheting and bankruptcy constraints.Controlled Block Proximal Linearized Method for Nonconvex and Nonsmooth Optimization
We consider multiblock nonconvex and nonsmooth optimization without assuming bounded iterates or coercivity of the original objective.Non-Ergodic Markovian Mirror Descent with Unbounded Noise
We analyze stochastic mirror descent for minimizing a convex objective expressed as an expectation under the stationary distribution of a Markov chain, using noisy subgradient observations along a…Certifying Distributionally Robust Decisions from Projection Observations
A scalar measurement can determine a robust decision without identifying the latent covariance or every decision risk.Sparse Sum-of-Squares Layers for Certified Learning
A central limitation of modern machine learning systems is the lack of formal certificates guaranteeing their behavior beyond empirical, sample-based evaluation.Online Covariance Estimation for Averaged Stochastic Gradient Descent on Riemannian Manifolds
We study online inference for averaged Riemannian stochastic gradient descent (RSGD) when the unknown parameter lies on a manifold.