In addition, two algorithms are presented for approximately solving fractional programming (FP) problems. The first algorithm is based on an objective space cut and bound method for solving convex FP ...

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, ...

Researchers utilise models that blend bilevel optimisation, mixed-integer nonlinear programming (MINLP) and distributed algorithms such as the alternating direction method of multipliers (ADMM) to ...

Introduction to theory and the solution of linear and nonlinear programming problems: including linear programming, duality, the simplex method, lagrangian duality, convex programming and KKT ...

And both algorithms can realistically handle only mildly nonlinear problems. The Maryland study quantifies exactly how much nonlinearity it can handle with a new parameter, R, which represents the ...

Lookup tables and Taylor series are two common methods for interpolating between experimentally gathered data or for generating a known function such as a ...

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 ...

View on Coursera Course Description This course continues our data structures and algorithms specialization by focussing on the use of linear and integer programming formulations for solving ...