Inverse optimisation and linear programming have emerged as crucial instruments in addressing complex decision-making problems where underlying models must be inferred from observed behaviour. At its ...

Linear semi-infinite programming (LSIP) is a branch of optimisation that focuses on problems where a finite number of decision variables is subject to infinitely many linear constraints. This ...

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, ...

Students will learn the foundations of linear programming, properties of optimal solutions and various solution methods for optimizing problems involving a linear objective function and linear ...

BEAVERTON, Ore.--(BUSINESS WIRE)--Gurobi Optimization, LLC today announced the release of Gurobi 9.0, the latest version of its industry-leading mathematical programming solver. Gurobi 9.0 delivers ...

Estimation errors or uncertainities in expected return and risk measures create difficulties for portfolio optimization. The literature deals with the uncertainty using stochastic, fuzzy or ...

Roughly, we will cover the following topics (some of them may be skipped depending on the time available). Linear Programming: Basics, Simplex Algorithm, and Duality. Applications of Linear ...

A first introduction to probability and statistics. This course will provide background to understand and produce rigorous statistical analysis including estimation, confidence intervals, hypothesis ...

As the title suggests, I have a problem in which I need to formulate an LP model. I'm supposed to work in Excel and use the Solver Add-in feature.<BR><BR>I've done several other problems already, but ...