$$ \begin{array}{ll} \underset{x \in \mathbb{R}^n}{\min} \quad & \langle c, x \rangle \\ \text{s.t.} \quad & L \leq A x \leq U, \\ & l \leq x \leq u . \end{array ...

Computer science involves much more than writing code. It blends technical knowledge —like programming, algorithms and data systems — with soft skills, such as communication and problem-solving.

Abstract: The algorithms and software tools for solving large-scale NP-hard MILP (mixed-integer linear programming) problems are analyzed, specifically focusing on the modeling of electric power ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. There is a need for design strategies that can support rapid and widespread deployment ...

ABSTRACT: This paper deals with linear programming techniques and their application in optimizing lecture rooms in an institution. This linear programming formulated based on the available secondary ...

The mathematics behind artificial intelligence (AI) and machine learning (ML) rely on linear algebra, calculus, probability, and statistics. These provide the foundation for developing the needed ...

Graph theory is an integral component of algorithm design that underlies sparse matrices, relational databases, and networks. Improving the performance of graph algorithms has direct implications to ...

This is the readme file for the Paper "Accelerating the Hungarian Algorithm: Computing Linear Assignments on CPUs, GPUs, and IPUs". IPU: We run our algorithm on the 1.325GHz Mk2 GC200 IPU. GPU: All ...

NVIDIA's cuOpt leverages GPU technology to drastically accelerate linear programming, achieving performance up to 5,000 times faster than traditional CPU-based solutions. The landscape of linear ...