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 30
Multiplicative Optimism for Constant Regret in Games
We introduce Multiplicatively Optimistic Regret Matching (MORM), an uncoupled learning rule for finite general-sum games.Scalable Incremental Robustness Analysis of Neural Network Feedback Systems
Semidefinite programming (SDP) certificates for feedback systems containing deep neural networks (NNs) typically scale with the total number of neurons, whereas small-gain tests are scalable but can…Locally Private Inference for Riemannian Stochastic Optimization
We develop inference for manifold-valued population minimizers when each observation belongs to a different participant and only locally private messages reach the analyst.Distributed Model Predictive Control with Connectivity-based Contracts
Teams of mobile robots rely on continuous communication with their neighbors for coordination, yet most distributed model predictive control (DMPC) schemes assume the communication network stays…Dynamic Generalized Gromov-Wasserstein Optimal Transport
Gromov--Wasserstein optimal transport (GW-OT) extends classical optimal transport by introducing structure-aware transport cost.Near-Optimal Pure Single-Loop Extragradient Method for Strongly Convex--Strongly Concave Minimax Optimization
We study smooth strongly convex--strongly concave minimax optimization with general nonlinear coupling in the deterministic unconstrained setting.The Bias of Nonlinear Two-Time-scale Stochastic Approximation under Constant Step-Sizes
Two-timescale stochastic approximation (TTSA) is a fundamental tool for analyzing coupled iterative algorithms in reinforcement learning, optimization, and stochastic control.Distributionally Robust Federated Learning with Multi-Source Data
Federated learning trains a shared model from private client data.The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings
We study first-order black-box convex optimization over an $\ell_p$-ball for objectives Lipschitz in the $\ell_q$-norm, solving in the affirmative the nonsmooth version of the COLT open question…Stable Movement for Nondual Lipschitz Convex Optimization: Efficiency and Nearly Optimal Oracle Rates
We study efficient algorithms for realizing the first-order oracle complexity of optimization of $G$-Lipschitz convex functions with respect to the $\ell_{q}$-norm over an $\ell_{p}$-ball of radius…Square Root Gauss-Newton iLQR
The iterative Linear Quadratic Regulator (iLQR) is a widely used algorithm for nonlinear trajectory optimization.Diverse and Adaptable Arm Coordination for Octopus-Crawling via Diffusion-Based Uncertainty-Aware Optimization
Octopus crawling motivates soft robots that exploit redundancy, yet discovering and organizing diverse coordination modes for adaptation remains challenging.Matching Multi-Loop Complexities with a Single Loop: Optimal Optimization Stationarity and Best-Known Game Stationarity in Nonconvex--Concave Minimax Optimization
We introduce a new single-loop algorithmic framework for smooth nonconvex--concave minimax optimization.The Attention Within: Consensus Dynamics in Selective State Space Models
Selective state space models (SSMs) have recently emerged as a compelling alternative to transformers, combining competitive performance with substantially improved inference efficiency.Beyond Quadratic Loss: The Stability Phase Diagram of Adam
Loss spikes are recurrent instabilities in neural-network training and can arise from multiple mechanisms.Gradient Descent with Stochastic Subspaces via Persistence of Memory
Stochastic subspace methods have gained popularity as gradient descent based techniques for large scale optimisation problems, especially in distributed settings.Rapid drone-based wildfire detection at a fraction of current prevention spending
Early wildfire detection is critical to prevent small ignitions from escalating into large-scale disasters, yet current monitoring systems lack a quantitative framework for allocating detection…A Geometric Theory of Decision Boundaries in Structured Markov Decision Processes
Classical dynamic programming represents optimal sequential decisions through value functions and policies.Revisiting Distributed Sign-Based Variance Reduction
Sign-based methods reduce communication costs in distributed environments, but aggregating local signs can introduce bias when data are heterogeneous.Regularity of a Multidimensional Principal-Agent Problem with Separable Effort Costs
This paper studies the regularity of the value function arising from a multidimensional continuous-time principal-agent model with separable, nonquadratic effort costs.