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, ...

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 ...

Two teams found different ways for quantum computers to process nonlinear systems by first disguising them as linear ones.

This paper presents an efficient method which accelerates the nonlinear analysis process. In this method, some path-following algorithms have been used to pass the limit points. A computer program is ...

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 ...

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, ...

The calculating methods are divided into analytic-calculating methods and numerical-calculating methods. As a kind of analytic calculation method, the universal unconstrained nonlinear programming ...

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 ...

DC-DC converters are extensively deployed across a range of new energy applications. However, traditional control methods that rely on accurate state space models encounter limitations in dc-dc ...