Applications and Advancements of Algorithms for Nonsmooth Optimization

Abstract

Optimization algorithms are the iterative routines that seek out the minimal or maximal value to a function under certain constraints. Research into optimization algorithms has been abundantly useful in a large variety of areas. Some examples include: tuning complex models to incorporate social dynamics, developing efficient manufacturing and distribution strategies, designing faster microchips, efficient management of power plants, financial portfolio optimization, and optimizing sequential testing procedures for hospital staff. In many applications of optimization, one is forced to optimize a problem with no particular structure to exploit. As such, advanced methods like the "Simplex Method" or "Interior Point Methods" cannot be applied. In such cases, one is forced to resort to using nonsmooth optimization methods on the problem. In this project we explore methods of advancing nonsmooth optimization algorithms, and apply such algorithms to a variety of real-world problems.

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