Sparse simplex projections for portfolio optimization

Anastasios Kyrillidis, Stephen Becker, Volkan Cevher, Christoph Koch

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.