This course examines formulation and solution of applicable optimization models, including linear, integer, nonlinear, and network problems, efficient algorithm methods, and use of computer modeling ...

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 ...

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 ...

Mathematical modelling and optimisation in packing problems constitute a critical research area that combines advanced algorithms, rigorous analytical formulations, and practical applications. This ...

Integer linear programming can help find the answer to a variety of real-world problems. Now researchers have found a much faster way to do it. The traveling salesperson problem is one of the oldest ...

The rise of AI, graphic processing, combinatorial optimization and other data-intensive applications has resulted in data-processing bottlenecks, as ever greater amounts of data must be shuttled back ...

Algorithms that zero in on solutions to optimization problems are the beating heart of machine reasoning. New results reveal surprising limits. Our lives are a succession of optimization problems.

We apply a linear Bayesian model to seismic tomography, a high-dimensional inverse problem in geophysics. The objective is to estimate the three-dimensional structure ...