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 2
Constrained defined-contribution pension management: A reparameterized neural network approach
This paper studies constrained defined contribution (DC) pension management under a two-factor financial market model.A Variational Quantum Algorithm for Multi-Skill Project Scheduling
Job scheduling is an NP-hard optimization problem with clear industrial relevance.Optimal Holding Times for Entanglement Swapping
We revisit the optimization of entanglement holding times in memory-based quantum repeaters for maximizing entanglement swapping throughput under different fidelity constraints - strict…Adaptive Multi Mode: Budget-Aware Routing of Optimizers By Online Landscape Probes
The best classical optimizer for a continuous black-box problem depends on its budget and landscape.Distributed Geometric Optimization and Control of Rigid Differential-Drive Formations from Relative-Position Measurements
Distance based formation control fixes the shape of a robot team but leaves its position and orientation unconstrained.Descending Everest, Part II: Oracle Complexity of Minimizing a Sum of Convex $(L_0,L_1)$-Smooth Functions
We study the minimization of the average of $n$ convex $(L_0,L_1)$-smooth functions: the Hessian norm of each function is bounded by an affine function of its gradient norm.Point interpolation and distributional universality in flow-based generative models
Motivated by the expressive power of neural ordinary differential equations in flow-based generative modeling, we study the approximation of probability measures by pushforwards generated by…How Risky Is Your Data-Driven Policy?
Data-driven algorithms are ubiquitous across industries, and there is an extensive literature on analyzing their \emph{average} performance.Computing Invariant Reachable Sets through Sum-of-Squares Programming
Funnels, time-varying invariant sets around nominal trajectories, certify that a closed-loop system tracks its plan, and can be composed into verified motion plans.A Bayesian-Geometric Reading of Classical Model-Based Optimal Design
The classical model-based optimal design criteria (D-, T-, L-, C-, and A-optimality) are usually motivated via the Cramér--Rao bound as optimizing scalar functions of the Fisher information matrix…Asymptotic Optimality of $cμ$-Rule for LLM Inference Scheduling under Memory Constraint
Large language model (LLM) inference, the process by which a pre-trained model receives an input prompt and generates output tokens sequentially, has become increasingly important with the…Finite Spectral Depth Controls Gaussian Entrance Rank and Avoidance of the First Gradient-Flow Saddle
Matrix factorization provides a simple model for studying the training dynamics of neural networks.Logarithmic Regret for Alternating Mirror Descent
Alternating online mirror descent (AltOMD) is a simple learning rule for zero-sum games, which improves upon simultaneous OMD numerically and theoretically.A simple adaptive accelerated optimistic dual averaging method for unconstrained convex optimization in $p$-norm geometry
Efficient preconditioning plays an important role in large-scale first-order optimization.A Fully Polynomial Approximation Algorithm for the Sherrington-Kirkpatrick Model
Montanari's incremental approximate message passing (IAMP) algorithm [FOCS 2019; SIAM J. Comput., 2025] and the zero-temperature formulation of El Alaoui, Montanari and Sellke (AMS) [Ann.Tight Lower Bounds under Matched High-Order Hölder Smoothness and Uniform Convexity
In this paper, we provide tight lower bounds for minimizing high-order Hölder-smooth and uniformly convex functions when their orders match.Relevance Propagation as Entropic Optimal Transport on Filippov Neural Networks: Sinkhorn Propagation and Region Relevance in Image Classifiers
Relevance propagation, which redistributes a network's prediction backward through the layers, is usually treated as a heuristic.Do Language Models Know Which Constraints Belong Together?
Dantzig-Wolfe reformulation strengthens the linear relaxation of a mixed-integer program by convexifying a chosen set of constraint families, and every candidate costs a column generation run.Conditional stopping guarantees for a folded parametrization of reduced Stokes vectors
Stopping decisions made in parametrization coordinates need not certify physically accurate estimates.Optimal Annuitization, Consumption, and Investment with Costly Capital Injections
We study a joint optimal decision of portfolio, consumption, and annuitization for a pension-plan manager who balances retirement consumption benefits against the cost of maintaining the solvency of…