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 ...

MG4C6.2 Mathematical Programming: Introduction to theory and the solution of linear and nonlinear programming problems: basic solutions and the simplex method, convex programming and KKT conditions, ...

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

Algorithms are turning up in the most unlikely places, promising to assert mathematical probability into corners of our lives where intuition, instinct and hunches have long held sway.

The algorithm presented here overcomes all of these shortcomings. Most significantly, it exhibits only a linear growth in the solution times based on the number of connections between nodes.

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

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, ...

Programming smart molecules: Machine-learning algorithms could make chemical reactions intelligent Date: December 12, 2013 Source: Harvard University Summary: Computer scientists have shown that ...

Dynamic Programming Algorithms in Computational Biology Publication Trend The graph below shows the total number of publications each year in Dynamic Programming Algorithms in Computational Biology.