Optimization Heuristics: A Tutorial

Forthcoming in “Numerical Methods and Optimization in Finance (2nd ed),” by M. Gilli, D. Maringer and E. Schumann

50 Pages Posted: 7 Jun 2019 Last revised: 25 Jul 2019

Date Written: December 31, 2018

Abstract

Heuristics are numerical methods that can solve difficult optimization models, such as models with multiple local optima, or with discontinuities in their objective functions and constraints. We provide a tutorial for using such methods, in which we tackle the classic subset-sum problem. The chapter is hands-on, and all ideas are illustrated through R code. We also show how the ideas of the tutorial can be used in two applications: selecting variables in a regression model, and computing weights for a portfolio of financial assets.

Keywords: heuristics, search, optimization, machine learning, model selection, portfolio selection, R, NMOF

Suggested Citation

Schumann, Enrico, Optimization Heuristics: A Tutorial (December 31, 2018). Forthcoming in “Numerical Methods and Optimization in Finance (2nd ed),” by M. Gilli, D. Maringer and E. Schumann. Available at SSRN: https://ssrn.com/abstract=3391756 or http://dx.doi.org/10.2139/ssrn.3391756

Enrico Schumann (Contact Author)

Independent ( email )

No Address Available

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