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 ...

To fulfill the 2 Core Courses, take two Core Courses from two different Core Areas. CSE Core Courses are classified into six areas: Introduction to CSE, Computational Mathematics, High Performance ...

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 ...

In this paper, we introduce an approach for constructing uncertainty sets for robust optimization using new deviation measures for random variables termed the forward and backward deviations. These ...

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 ...

Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...

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.I've done several other problems already, but this ...