Design as strategy—it’s effective at shaping systems and crafting end products.

Neel Somani has built a career at the intersection of mathematical optimization and large-scale infrastructure. A UC Berkeley alumnus who sharpened his skills as a Quantitative Researcher at Citadel, ...

NVIDIA's GPU-accelerated cuOpt engine discovers new solutions for four MIPLIB benchmark problems, outperforming CPU solvers with 22% lower objective gaps. NVIDIA's cuOpt optimization engine has found ...

Optimization problems often involve situations in which the user's goal is to minimize and/or maximize not a single objective function, but several, usually conflicting, functions simultaneously. Such ...

Long sales cycles, low conversion volume, and multi-stage purchase journeys make measurement and attribution harder, creating real obstacles to campaign optimization. For B2Bs and brands selling ...

We present OPT-BENCH, a benchmark comprising 20 machine learning tasks and 10 NP problems, specifically designed to assess large language models’ (LLMs) ability to solve problems with large search ...

Abstract: Many complex problems encountered in both production and daily life can be conceptualized as combinatorial optimization problems (COPs). Many ad-hoc deep learning methods have been proposed ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

Abstract: A wide range of real applications can be modelled as the multiobjective traveling salesman problem (MOTSP), one of typical combinatorial optimization problems. Meta-heuristics can be used to ...