# Strongly convex stochastic online optimization on a unit simplex with application to the mixing least square regression

## TL;DR

This paper introduces a novel stochastic online mirror descent method for strongly convex optimization on a unit simplex, with applications to mixing least squares regression, enhancing estimation accuracy in non-Euclidean settings.

## Contribution

It presents a new approach for mixing least squares regression using stochastic online mirror descent tailored for non-Euclidean spaces.

## Key findings

- Effective estimation on the unit simplex
- Improved convergence in non-Euclidean settings
- Applicability to mixing least squares regression

## Abstract

In this paper we propose a new approach to obtain mixing least square regression estimate by means of stochastic online mirror descent in non-euclidian set-up.

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Source: https://tomesphere.com/paper/1703.06770