The Minimum $L_2$-Distance Projection onto the Canonical Simplex: A Simple Algorithm
Optimization and Control
2024-04-02 v1
Abstract
We consider the minimum distance projection in the -norm from an arbitrary point in an -dimensional, Euclidian space onto the canonical simplex. It is shown that this problem reduces to a univariate problem that can be solved by a simple algorithm. This optimization problem occurs in the setting of credit risk, where one has stochastic matrices that describe transition probabilities between different credit ratings, and one wants to determine the roots of these matrices, or close approximations to them.
Keywords
Cite
@article{arxiv.2404.00002,
title = {The Minimum $L_2$-Distance Projection onto the Canonical Simplex: A Simple Algorithm},
author = {Hans J. H. Tuenter},
journal= {arXiv preprint arXiv:2404.00002},
year = {2024}
}
Comments
Algo Research Quarterly (ARQ) was the research journal of Algorithmics Inc., a Toronto-based company specializing in enterprise risk-management solutions. The journal was published over the years 1998 to 2002