Finite-Sample Optimality and Constraint Satisfaction: Learning-Based Optimal Control in Dynamic Dispatch Networks

Dynamic dispatch networks in logistics and transportation require real-time, constraint-aware decision-making under stochastic demand. This paper bridges mathematical optimization, optimal control theory, and reinforcement learning by establishing non-asymptotic theoretical guarantees for learning-based optimal control in constrained stochastic dispatch systems. We formulate the problem as a constrained Markov decision process, enforce feasibility via a projection-based policy … Read more

Second-Order Optimality Conditions for Bilevel Optimization Problems Using Parabolic Directional Derivatives

This paper studies the second-order properties of a class of inequality-constrained bilevel programming problems. First-order optimality conditions for the existence of solutions to bilevel optimization problems are derived using the first-order directional derivative of the optimal solution function of the lower-level problem in the seminal paper by Dempe (1992). In this work, we prove that … Read more

Convergence of the Frank-Wolfe Algorithm for Monotone Variational Inequalities

We consider the Frank-Wolfe algorithm for solving variational inequalities over compact, convex sets under a monotone \(C^1\) operator and vanishing, nonsummable step sizes. We introduce a continuous-time interpolation of the discrete iteration and use tools from dynamical systems theory to analyze its asymptotic behavior. This allows us to derive convergence results for the original discrete … Read more

Finding Short Paths on Simple Polytopes

We prove that computing a shortest monotone path to the optimum of a linear program over a simple polytope is NP-hard, thus resolving a 2022 open question of De Loera, Kafer, and Sanit\`{a}. As a consequence, finding a shortest sequence of pivots to an optimal basis with the simplex method is NP-hard. In fact, we … Read more

Negative Momentum for Convex-Concave Optimization

This paper revisits momentum in the context of min-max optimization. Momentum is a celebrated mechanism for accelerating gradient dynamics in settings like convex minimization, but its direct use in min-max optimization makes gradient dynamics diverge. Surprisingly, Gidel et al. 2019 showed that negative momentum can help fix convergence. However, despite these promising initial results and … Read more

A Nesterov-Accelerated Primal-Dual Splitting Algorithm for Convex Nonsmooth Optimization

We investigate the integration of Nesterov-type acceleration into primal-dual methods for structured convex optimization. While proximal splitting algorithms efficiently handle composite problems of the form min_x f(x) + g(x) + h(Kx), accelerating their convergence with respect to the smooth term f is notoriously challenging due to the rotational dynamics in the primal-dual space. In this … Read more

Benders Cut Filtering for Affine Potential-Based Flow Problems with Robustness Scenarios and Topology Switching

Many large-scale optimization problems decompose into a master problem and scenario subproblems, a structure that can be exploited by Benders decomposition. In Benders decomposition, each iteration may generate many cuts from scenario subproblems, and adding all of them as constraints then causes the master problem to grow rapidly. These are constraints that may need to … Read more

Multi-Fidelity Benders Decomposition for Generation, Storage, and Transmission Expansion Planning

Modern energy grid expansion planning, by necessity, includes timeseries data to accurately model storage and renewable assets. Representative time periods are commonly used as a way to decrease problem size and therefore mitigate the increased complexity from this inclusion. However, there are many choices around these representative periods: length; location in planning horizon; boundary conditions. … Read more

A polynomial-time solvable class of sparse box-constrained polynomial optimization problems

We study the problem of minimizing a multivariate polynomial function over the unit hypercube. By representing the polynomial through a hypergraph and exploiting its sparsity structure, we establish a new sufficient condition under which the problem can be solved in time polynomial in the encoding length of the input. Our approach identifies a subset of … Read more

Rethinking Last-Mile Routing at Scale: Near-Linear Planning on Commodity Hardware

This paper presents a practical architecture for last-mile delivery routing at scales reaching one million stops under realistic operational conditions, including vehicle capacity, package volume, route stop limits, and time windows. Unlike conventional systems that require pre-partitioning or large-scale infrastructure, the proposed system addresses the full fleet planning problem through a single coherent planning pipeline … Read more