Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

This is an advanced undergraduate course on algorithms. This course examines such topics as greedy algorithms, dynamic programming, graph algorithms, string processing, and algorithms for ...

Learning to code doesn’t require new brain systems—it builds on the ones we already use for logic and reasoning.

Algorithm design and analysis is fundamental to all areas of computer science and gives a rigorous framework for the study optimization. This course provides an introduction to algorithm design ...

Daniel Lokshtanov’s work explores the limits of what computers can solve, paving the way for advances in artificial intelligence and computational efficiency.

In this paper, we propose a new branch and bound algorithm for the solution of large scale separable concave programming problems. The largest distance bisection (LDB) technique is proposed to divide ...

Parts of the brain are "rewired" when people learn computer programming, according to new research. Scientists watched ...

This paper studies a class of integer programming problems in which squares of variables may occur in the constraints, and shows that no computing device can be programmed to compute the optimum ...