Second, the conversion to a single-objective optimization problem involves additional constraints. Third, since most real-world goal programming problems involve nonlinear criterion functions, the ...

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 ...

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 ...

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, ...

Researchers have long hoped they could similarly tame nonlinear problems with clever quantum algorithms. The new approaches disguise that nonlinearity as a more digestible set of linear approximations ...

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

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 ...

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 ...