These are math’s most famous open questions. Solve one, and you’ll win a $1-million prize—but it’s only happened once since ...

Learn how to solve problems using linear programming. A linear programming problem involves finding the maximum or minimum value of an equation, called the objective functions, subject to a system of ...

Abstract: Unit commitment problems can be solved more efficiently with mixed integer linear programming solvers when more preferred hyperparameters are configured. We propose a learning approach to ...

For the C implementation on GPUs (recommended for benchmarking), please visit the following repository: $$ \begin{array}{ll} \underset{x \in \mathbb{R}^n}{\min} \quad ...

Graph theory is like a secret weapon for tackling real-world challenges. From optimizing delivery routes to powering social media friend suggestions, graphs—nodes and edges—help us make sense of ...

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific ...

Abstract: This work describes a MATLAB tool developed in the context of a didactic application toolbox that implements some advanced optimization and decision support methodologies, intended for use ...

Since its creation more than two decades ago by Daniel Spielman (above) and Shang-hua Teng, smoothed analysis has been used to analyze performance of algorithms other than the simplex method, ...

Mixed-Integer Linear Programming (MILP) plays an important role across a range of scientific disciplines and within areas of strategic importance to society. The MILP problems, however, suffer from ...

Image segmentation technology has been widely applied in industry, agriculture, military fields, etc. Among all the image segmentation methods, thresholding is one of the most widely used, because it ...