Stochastic Bilevel Optimization for the Network Design of Multimodal Transit Systems with Heterogeneous Rider Preferences under Uncertain Travel Times and Demand

Categories Bilevel Optimization, Stochastic Programming, Transportation

Designing efficient and user-friendly multimodal transit networks is critical for modern urban mobility. We study a novel stochastic multimodal transit network design problem that integrates fixed-route services with on-demand shuttles, explicitly accounting for heterogeneous rider preferences, uncertain travel times, and passenger demand. The hierarchical decision-making process is modeled using a two-stage stochastic bilevel optimization problem, … Read more

Nested Benders Decomposition for Large-Scale Multi-Follower Bilevel Optimization

Categories Bilevel Optimization, Parallel Algorithms, Transportation Tags , , , ,

We propose a scalable nested Benders decomposition (BD) framework for single-leader, multi-follower bilevel optimization problems. The proposed framework is applicable to bilevel optimization problems in which each follower solves a linear program and is particularly well suited for instances involving a large number of followers. By identifying the upper-level decisions as complicating variables, the method … Read more

Stochastic convergence of parallel asynchronous adaptive first-order methods

Categories Data Science Algorithms, Stochastic Programming, Unconstrained Optimization Tags , , , , ,

A new class of asynchronous adaptive first-order optimization methods is introduced, comprising asynchronous variants of several popular algorithms. Versions of these methods using momentum and/or inexact normalization are also considered. The convergence of methods in the class on non-convex functions is analyzed in a fully stochastic setting, and is shown to be (up to logarithmic … Read more

Non-convergence Analysis of Probabilistic Direct Search

Categories Nonlinear Optimization, Stochastic Programming Tags , , , ,

We present a non-convergence theory for probabilistic direct search, a randomized derivative-free optimization method, where non-convergence means the failure to produce iterates that achieve stationarity asymptotically. The motivation is to understand whether the submartingale-like assumption in the existing convergence theory is essential or merely an artifact of the analysis techniques. For convex objectives, we prove … Read more

A path-following framework on fiber bundle for variational inequalities

Categories Complementarity and Variational Inequalities Tags , , ,

This paper proposes a path-following framework for finite-dimensional variational inequalities with arbitrary continuous functions and compact convex domains. The approach first approximately reduces a general variational inequality to a smooth variational inequality on a simplex. Its key innovation is to formulate the smooth variational inequality on a simplex on a fiber bundle called the fixed-point … Read more

A Parameter-Free Restart Scheme with Only a Parallelizable $\log\log(1/\epsilon)$ Overhead

Categories Convex Optimization, Nonsmooth Optimization Tags , , ,

It is well-known that first-order methods can offer accelerated convergence rates in the presence of growth structures. Restarting schemes provide a general tool for such speed-ups. These schemes typically either require unrealistic problem knowledge, incur logarithmic overhead factors in oracle complexity, and/or have a nontrivial initial burn-in phase. We present a parameter-free approach for restarting … Read more

Disjunctive Sum of Squares

Categories Cone Programming, Convex Optimization, Semi-definite Programming Tags , , , , , , ,

We introduce the concept of disjunctive sum of squares for certifying nonnegativity of polynomials. Unlike the popular sum of squares approach where nonnegativity is certified by a single algebraic identity, the disjunctive sum of squares approach certifies nonnegativity with multiple algebraic identities which can be found in parallel. Our main result is a disjunctive Positivstellensatz … Read more

Log-Averaged Mirror Prox for Fast, Large-Scale Optimal Transport in Linear Space

Categories Convex Optimization, Parallel Algorithms Tags , , , ,

We propose Log-Averaged Mirror Prox (LAMP), a linear-space primal-dual method for large-scale optimal transport. LAMP implements primal mirror prox updates by tracking an averaged dual sequence, reducing storage complexity from \({O}(nm)\) to \({O}(n+m)\) while preserving dense, GPU-friendly reductions. Consequently, LAMP preserves the last-iterate \(\widetilde{{O}}( nm\varepsilon^{-1})\) arithmetic complexity of conservatively parameterized primal-dual mirror prox. We further … Read more