Optimization in Data Science

Machine Learning Algorithms for Assisting Solvers for Decision Optimization Problems

Categories Applications - OR and Management Sciences, Integer Programming, Optimization in Data Science Tags , , , , , , , ,

Combinatorial decision problems lie at the intersection of Operations Research (OR) and Artificial Intelligence (AI), encompassing structured optimization tasks such as submodular selection, dynamic programming, planning, and scheduling. These problems exhibit exponential growth in decision complexity, driven by interdependent choices coupled through logical, temporal, and resource constraints.  Classical optimization frameworks—including integer programming, submodular optimization, and … Read more

Closing the Gap: Efficient Algorithms for Discrete Wasserstein Barycenters

Categories Integer Programming, Optimization in Data Science Tags , , , ,

The Wasserstein barycenter problem seeks a probability measure that minimizes the weighted average of the Wasserstein distances to a given collection of probability measures. We study the discrete setting, where each measure has finite support — a regime that frequently arises in machine learning and operations research. The discrete Wasserstein barycenter problem is known to … Read more

Optimizing pricing strategies through learning the market structure

Categories Optimization in Data Science, Yield Management Tags , , ,

This study explores the integration of market structure learning into pricing strategies to maximize revenue in e-commerce and retail environments. We consider the problem of determining the revenue maximizing price of a single product in a market of heterogeneous consumers segmented by their product valuations; and analyze the pricing strategies for varying levels of prior … Read more

Adaptive Conditional Gradient Descent

Categories Nonlinear Optimization, Optimization in Data Science Tags , , , , , , ,

Selecting an effective step-size is a fundamental challenge in first-order optimization, especially for problems with non-Euclidean geometries. This paper presents a novel adaptive step-size strategy for optimization algorithms that rely on linear minimization oracles, as used in the Conditional Gradient or non-Euclidean Normalized Steepest Descent algorithms. Using a simple heuristic to estimate a local Lipschitz … Read more

Consolidation in Crowdshipping with Scheduled Transfer Lines: A Surrogate-Based Network Design Framework

Categories Combinatorial Optimization, Optimization in Data Science, Transportation Tags , , ,

Abstract: Crowdshipping has gained attention as an emerging delivery model thanks to advantages such as flexibility and an asset-light structure. Yet, it chronically suffers from a lackof mechanisms to create and exploit consolidation opportunities, limiting its efficiency and scalability. This work contributes to the literature in two ways: first, by introducing a novel consolidation concept … Read more

Progressively Sampled Equality-Constrained Optimization

Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Optimization in Data Science Tags , , , ,

An algorithm is proposed, analyzed, and tested for solving continuous nonlinear-equality-constrained optimization problems where the constraints are defined by an expectation or an average over a large (finite) number of terms. The main idea of the algorithm is to solve a sequence of equality-constrained problems, each involving a finite sample of constraint-function terms, over which … Read more

Machine Learning Algorithms for Improving Black Box Optimization Solvers

Categories Global Optimization, Nonlinear Optimization, Optimization in Data Science Tags , , , , ,

Black-box optimization (BBO) addresses problems where objectives are accessible only through costly queries without gradients or explicit structure. Classical derivative-free methods—line search, direct search, and model-based solvers such as Bayesian optimization—form the backbone of BBO, yet often struggle in high-dimensional, noisy, or mixed-integer settings. Recent advances use machine learning (ML) and reinforcement learning (RL) to … Read more

Optimal diagonal preconditioning beyond worst-case conditioning: theory and practice of omega scaling

Categories Convex and Nonsmooth Optimization, Nonsmooth Optimization, Optimization in Data Science Tags , , , ,

Preconditioning is essential in many areas of mathematics, and in particular is a fundamental tool for accelerating iterative methods for solving linear systems. In this work, we study optimal diagonal preconditioning under two distinct notions of conditioning: the classical worst-case \(\kappa\)-condition number and the averaging-based \(\omega\)-condition number. We observe that \(\omega\)-optimal preconditioning generally outperforms \(\kappa\)-optimal … Read more

A Minimalist Bayesian Framework for Stochastic Optimization

Categories Global Optimization, Optimization in Data Science, Stochastic Programming Tags , , ,

The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all parameters can hinder the incorporation of complex structural constraints. We introduce a minimalist Bayesian framework that places a prior only on the component of interest, such as the location of the optimum. Nuisance parameters are … Read more