Solve Smarter, Not Harder: Rethinking Solver Strategy for Large-Scale Optimization

Reimagining optimization with scalable and reproducible solver strategies

For analytics leaders solving complex use cases, such as yield maximization, logistics planning, or pricing optimization, the pressure is often to build quickly. A clear understanding of the mathematical structure of the problem and the right solver strategy can help teams reduce compute costs and the risk of inefficiencies, and improve scalability.

This white paper presents a thorough, real-world assessment of open-source solvers for large-scale optimization, grounded in our expertise in Operations Research (OR). Drawing from our work with Fortune 500 clients across domains, we explore how problem formulation, algorithm selection, and solver choice impact the performance and agility of optimization systems at scale.

In this white paper, you’ll discover how Tiger Analytics’ Operations Research experts:

Identify the right solver strategies for large-scale optimization problems
Map mathematical problem types to appropriate open-source solvers and algorithms
Evaluate solver trade-offs across compute time, memory usage, and reproducibility
Make informed, scalable decisions when applying OR techniques to high-impact use cases