Applied Math · Mathematics & Statistics
Optimization & Control
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754 papers, from 31 August 2026 to 5 November 2026: 698 arXiv preprints and 56 OpenAlex records.
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Newest papers, page 33
Equilibrium bias and convergence in augmented primal--dual dynamics with sampled constraints
This work studies the stability and convergence of augmented primal-dual dynamics when constraint values are estimated from samples.Riemannian ascent--descent for nonconvex nonconcave minimax landscapes: convergence to basin saddle points and applications to distributionally robust optimization
We study a class of distributionally robust optimization (DRO) problems for the statistical risk problem, formulated as minimax problems over the product of a Euclidean space and a Riemannian…Inference for Newton Methods with Accelerated Sketch-and-Project via Random Scaling
We study an online sketched Newton method that approximates the Newton direction at each step via a state-of-the-art sketching solver, called the generalized accelerated sketch-and-project solver…Linear Exponential Quadratic Gaussian Covariance Steering
We formulate and analyze the linear exponential quadratic Gaussian (LEQG) covariance steering problem in continuous time over a given deadline (finite time horizon).Adaptive Agent Design
We consider an agent acting against a general non-Markovian environment.LettuceVisSim: A Simulator That Generates Lettuce Image Time-series for Vision-Based Reinforcement Learning
Vision-based reinforcement learning holds strong potential for decision-making in controlled environment agriculture (CEA).Learning-Augmented Optimization for Strategic Two-Echelon Spare Parts Network Design
We study the strategic design of a two-echelon spare-parts inventory network where evaluating each candidate topology requires an expensive inventory optimization model.Optimizing Geoengineering Interventions Using Differentiable Climate Models
The deployment of a geoengineering program to cool Earth's climate may be imminent.SIMS: Scale-Invariant Merit-Function-Based Scalarization for Multi-Task Learning
Multi-task learning (MTL) requires navigating unavoidable trade-offs among competing objectives.High-Probability Convergence of SGD via Batched Updates
Stochastic gradient descent (SGD) is the primary workhorse for large-scale optimization.Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and Hölder Smoothness
Classical convergence guarantees for stochastic gradient methods typically assume Lipschitz-smooth objectives and finite-variance gradient noise, both frequently violated in practice.Inverse Learning of the Altruism and Cost Level in Mixed-Individual Mean Field Games
Understanding how humans respond to incentives, both at the individual and collective levels, is crucial to the design of effective policies.A family of spectral conjugate gradient algorithms derived by least-squares approximations based on a modified quasi--Newton update with application to a revised robust binary classification model
We develop a spectral three-term modification of the classic Hestenes--Stiefel conjugate gradient algorithm, preserving its anti-jamming characteristic and, simultaneously, taking care of the…Differential Realizability of Static Control Allocation in Multirotors: An Impossibility under Nonredundant Full Actuation and a Pseudoinverse Obstruction under Redundant Actuation
Control allocation for multirotors with bidirectional propellers is commonly formulated in signed-thrust variables, where the wrench map is linear.Mechanism Design for Ad-Supported Platforms
Many digital platforms earn revenue by selling content access and displaying ads.EGGROLL, Unrolled: Understanding and Improving Low-Rank Evolution Strategies at Scale
EGGROLL makes evolution strategies (ES) practical for LLMs by replacing dense Gaussian weight perturbations with low-rank Gaussian products, often of rank one.Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set
Data-driven distributionally robust optimization (DRO) typically treats the conditional outcome law as fixed and uses ambiguity sets to capture estimation error.Convex Optimization with Nested Evolving Feasible Sets (CONES) under Time-Varying Loss Functions
Convex Optimization with Nested Evolving Feasible Sets (CONES)} was introduced in \cite{CONESVaze} where the objective function \(f\) remains fixed but the feasible region evolves over time as a…Sparsity Regularized and Robust Mean Variance Portfolio Selection Under Ellipsoidal Uncertainty
We investigate mean-variance portfolio selection with an $\ell_0$-penalty to promote sparsity in asset allocations.AdamX: Cosine similarity meets gradient descent
We introduce AdamX, a first-order optimizer that incorporates cosine similarity as an adaptive mechanism for controlling update magnitudes.