The R Journal: accepted article

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LP Algorithms for Portfolio Optimization: The PortfolioOptim Package PDF download
Andrzej Palczewski

Abstract The paper describes two algorithms for financial portfolio optimization with the following risk measures: CVaR, MAD, LSAD and dispersion CVaR. These algorithms can be applied to discrete distributions of asset returns since then the optimization problems can be reduced to linear programs. The first algorithm solves a simple recourse problem as described by Klein Haneveld and Van der Vlerk (2006) using Benders decomposition method. The second algorithm finds an optimal portfolio with the smallest distance to a given benchmark portfolio and is an adaptation of the least norm solution (called also normal solution) of linear programs due to Zhao and Li (2002). The algorithms are implemented in R in the package PortfolioOptim.

Received: 2017-07-23; online 2018-05-21, supplementary material, (1.4 Kb)
CRAN packages: fPortfolio, PortfolioAnalytics, Rglpk, quadprog, DEoptim, GenSA, psoptim, parma, nloptr, PortfolioOptim
CRAN Task Views implied by cited CRAN packages: Optimization, Finance

CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

  author = {Andrzej Palczewski},
  title = {{LP Algorithms for Portfolio Optimization: The PortfolioOptim
  year = {2018},
  journal = {{The R Journal}},
  url = {}