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 5
Physically Constrained Kinematic Calibration for High-Precision Robotic Needle Guidance
Percutaneous needle insertion is a common practice in minimally invasive procedures whose clinical success depends on accurate needle placement.Improving the Optimization Landscape of Matrix Completion with $ε$-close Surrogates
Non-convex optimization is central to machine learning and AI, yet unlike its convex counterpart, it still lacks a general and principled framework.Improved Approximation Guarantees for Periodic-Review Stochastic Inventory Control
In periodic-review stochastic inventory control, replenishment policies must balance holding and backlogging costs while responding to uncertain demands and evolving forecasts that may be…Distributed Grid Search for Hyperparameter Tuning
The performance of many distributed optimization algorithms over peer-to-peer networks strongly depends on the choice of their hyperparameters.A complete analytical characterization of optimal collision-free motion for two robots
Collision-free motion planning for multi-agent robotic systems remains an active research area because of the complexity of coordinating robot trajectories, with applications ranging from…Underdamped Stochastic Optimal Transport with Landauer boundary conditions: machine learning and analytical solution
Finding optimal transport protocols for underdamped stochastic processes is a difficult yet important problem in control theory.Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning
Autonomous inspection of a target spacecraft is typically the initial step in on-orbit servicing.Risk-Sensitive Mission Planning for Multi-Target On-Orbit Inspection via Distributional Reinforcement Learning
Multi-target on-orbit servicing missions require planning tools that can adapt to evolving operational conditions while accounting for the outcome variability inherent to safety-critical spaceflight.Overcoming the Representational Limitations of Amplitude Encoding for Quantum Neural Networks
Quantum Neural Networks (QNNs) offer a framework for learning functions from classical data, but their expressive power depends critically on how inputs are encoded into quantum states.Integrating Low-Thrust Transfer Dynamics into POMDP Planning for On-Orbit Servicing
Recent developments in both high-thrust (HT) and low-thrust (LT) propulsion technologies broaden design options for on-orbit servicing (OOS) missions.Variance Reduction for Decentralized Stochastic Optimization with Multiple Local Updates
Multiple local updates are widely used in decentralized optimization to enhance communication efficiency, often in conjunction with stochastic gradients when exact gradient computation is costly.Near-Optimal Second-Order Convex Finite-Sum Optimization under Individual Smoothness
We study the stochastic second-order oracle complexity of solving smooth convex finite-sum optimization with $n$ components.Exact Theory and Optimal Stepsizes for Stochastic Gradient Descent in Smooth Convex Minimization
This work considers the design of stepsize schedules for stochastic gradient descent in smooth convex optimization.The Quartic Gap Between Quantum and Dequantized Algorithms for Linear Algebra Is Tight
Quantum algorithms for linear algebra that access a matrix $A$ through quantum random access memory (QRAM) prepare quantum states proportional to the solutions of linear systems and related problems.Cost-Aware Stopping for Freeze-Thaw Bayesian Optimization: When Does One-Step Lookahead Suffice?
Freeze-thaw Bayesian optimization (BO) evaluates candidates in resumable increments but usually stops at a budget or convergence test.Regret-Optimal Wasserstein-Robust Linear Regression
Distributionally robust optimization (DRO) minimizes worst-case risk under distributional uncertainty, but its decisions can be suboptimal in terms of their excess risk, or regret.The Prior-Force Family of Implicit Denoiser Priors and Its Decisions in Linear Inverse Problems
Implicit priors given by Gaussian denoisers successfully regularize a wide range of inverse problems in approaches such as plug-and-play (PnP), regularization by denoising (RED) and score-based…ProLand: A New Infeasible Manifold Optimization Method for LoRA Fine-Tuning
Low-rank adaptation (LoRA) enables parameter-efficient fine-tuning but can suffer from redundant basis directions.Optimistic Gradient Algorithms with Randomized Coordinate and Variance Reduction Strategies for Composite Inclusions
In this paper, we consider a class of possibly nonmonotone finite-sum composite inclusions in which evaluating all components or updating all coordinates at every iteration can be computationally…Measurement-angle control of a continuously monitored quantum oscillator: purification limits and optimal protocols
We study the dynamic purification of a continuously monitored quantum harmonic oscillator coupled to a thermal bath, where the control is the quadrature measurement angle of up to two detectors with…