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, page 6
LLM-Assisted Optimization for Simulation-Based Redesign
Engineering design frequently involves redesigning a prior solution to adapt to changes in context, such as material prices or system requirements.There Is No Free Lunch in RLVR: Every pass@1 Gain Has a pass@k Price
Reinforcement learning with verifiable rewards (RLVR) makes a language model solve more problems on the first try and fewer when given many tries: it raises pass@1 while the base model overtakes it…Information-Driven Collaborative Autonomy for Heterogeneous UAV/UGV Teams
This paper considers coordinating a heterogeneous team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) traversing a domain containing an unknown number of potentially…Safe Multi-Objective Reinforcement Learning: Zero Constraint Violation and Primal-Averaging Convergence
Many applications of multi-objective reinforcement learning (MORL) require policies that balance conflicting objectives while satisfying safety constraints.Dobb: Shard-Local Preconditioning in Block-Diagonal Eigenbases
Training language models whose parameters and optimizer state exceed a single node's memory requires sharding these tensors across devices.Conic-Lifting for Learning-rate-free Convex Optimization
A central topic in online and stochastic optimization is to design algorithms that do not require tuning of hyperparameters.Carbon-Aware Electric Vehicle Routing Problem with Decision-Focused Learning
Electric vehicles produce no tailpipe emissions, but charging them produces emissions that depend on the time-varying generation mix of the electricity consumed.Adaptive Federated Optimization Across Smooth and Nonsmooth Regimes
AdaGrad-style methods can simultaneously adapt to both the order and constants of smoothness in centralized optimization.Implicit Bias of (Stochastic) Gradient Descent on Deep Linear Diagonal Networks
Modern machine learning models are often overparameterized, i.e., have more parameters than training data.When to spend privacy: optimal allocation in private learning
Privacy accounting bounds the cumulative privacy cost of Differentially Private Stochastic Gradient Descent but does not determine how to allocate a fixed budget across iterations.Finite-Sample LTI System Identification from a Misspecified Finite Model Set
We consider the problem of finite-sample identification of an unknown LTI system from a finite hypothesis class.Adaptive search with path-constrained mobile sensors using multi-arm bandits
We study a constrained sequential decision problem for detecting regions of interest from noisy online measurements collected by a team of energy-constrained mobile sensors.Single-Temperature Ranked Greedy Cluster Exchange for Max-Cut on Two-Dimensional Tori
Ground-state search in two-dimensional Ising spin glasses on large toroidal lattices remains challenging for local-update heuristics because rugged energy barriers impede relaxation across…The Value of Experiments in Assortment Optimization
Retailers can learn customer preferences by experimenting with different assortments.Learning Stabilizing Latent Feedback Controllers via Multi-Step Backward Losses
Can learned latent dynamics models support the design of feedback controllers that provably stabilize the original system?Immersed Boundary Electrodiffusion in the Wasserstein Gradient Flow Formalism: Global Well-Posedness, Non-Equilibrium Thermodynamics, and Singular Asymptotics
We introduce a unified optimal transport framework for the Immersed Boundary (IB) electrodiffusion active pump model.Neural Network Methods in the 2BSDE Formalism for Solving the High-Dimensional Black--Scholes Equation with Transaction Costs
A 2BSDE system is written down for a $(D+1)$-dimensional Black--Scholes partial differential equation (PDE) with the Leland transaction cost model which incorporates the sign of the curvature.CuPIQP: A Batched and Differentiable QP Solver on GPUs for Robot Learning and Control
Quadratic programs (QPs) are fundamental building blocks of many robotic tasks such as inverse kinematics, whole-body control, and model predictive control.When can Optimally Trained Transformers Achieve Near-Lossless Token Prediction for non-Markovian Data?
There exists little mathematical theory explaining why Transformer architectures are empirically successful even for non-Markovian data.Stochastic and Noisy Oracles in Smooth Continuous Optimization
We review models of inexact computational oracles in smooth continuous optimization and their implications for the convergence, computational complexity, and attainable accuracy of optimization…