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) .

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 ...

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

COMP_SCI 396, 496: Advanced Algorithm Design through the Lens of Competitive Programming VIEW ALL COURSE TIMES AND SESSIONS Prerequisites CS 336 or Permission of Instructor Description This is an ...

Introduction to theory of algorithms and basics of Python programming. Algorithmic thinking: Do you know how to multiply integers? Basic toolkit for the design and analysis of algorithms, and an ...

Successive Linear Programming (SLP) algorithms solve nonlinear optimization problems via a sequence of linear programs. They have been widely used, particularly in the oil and chemical industries, ...

Introduction to theory of algorithms and basics of Python programming. Algorithmic thinking: Do you know how to multiply integers? Basic toolkit for the design and analysis of algorithms, and an ...

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 ...