Title :
Sparse simplex projections for portfolio optimization
Author :
Kyrillidis, Anastasios ; Becker, Steffen ; Cevher, Volkan ; Koch, Christian
Author_Institution :
Lab. for Inf. & Inference Syst., EPFL, Lausanne, Switzerland
Abstract :
We derive efficient sparse projections onto the simplex and its extension, and illustrate how to use them to solve high-dimensional learning problems such as portfolio selection with non-convex constraints. To this end, we study the following sparse Euclidean projections.
Keywords :
concave programming; investment; learning (artificial intelligence); high-dimensional learning problems; nonconvex constraints; portfolio optimization; portfolio selection; sparse Euclidean projections; sparse simplex projections; Covariance matrices; Indexes; Laboratories; Monte Carlo methods; Optimization; Portfolios; Vectors;
Conference_Titel :
Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE
Conference_Location :
Austin, TX
DOI :
10.1109/GlobalSIP.2013.6737104
