This course continues our data structures and algorithms specialization by focussing on the use of linear and integer programming formulations for solving algorithmic problems that seek optimal ...

Advanced study in models of computation, programming languages and algorithms with a specific focus on concurrent programming. The course includes models of computation, programming language paradigms ...

It covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) .

Dynamic programming (DP) algorithms have become indispensable in computational biology, addressing problems that range from sequence alignment and phylogenetic inference to RNA secondary structure ...

GCSE Computer Science Computational thinking, algorithms and programming learning resources for adults, children, parents and teachers.

Probabilistic Programming and Inference Algorithms Publication Trend The graph below shows the total number of publications each year in Probabilistic Programming and Inference Algorithms.

Jonathan Eckstein, Nonlinear Proximal Point Algorithms Using Bregman Functions, with Applications to Convex Programming, Mathematics of Operations Research, Vol. 18, No. 1 (Feb., 1993), pp. 202-226 ...

If you are doing X11, for example, you can go low-level or pick any of a number of high-level libraries. The tutorial assumes you understand C and basic Linux programming. It has two sections, really.

This paper presents a constraint logic programming model for the traveling salesman problem with time windows which yields an exact branch-and-bound optimization algorithm without any restrictive ...