# Partial minimization of strict convex functions and tensor scaling

## TL;DR

This paper introduces a partial minimization algorithm for strict convex functions, demonstrating geometric convergence and connecting it to classical methods like conjugate gradient and Sinkhorn scaling for matrices and tensors.

## Contribution

It presents a novel partial minimization algorithm with proven convergence for convex functions and links tensor and matrix scaling to partial minimization of log-convex functions.

## Key findings

- Algorithm converges geometrically to the unique minimum.
- Connection established between Sinkhorn scaling and partial minimization.
- Generalization of conjugate gradient method for quadratic polynomials.

## Abstract

Assume that f is a strict convex function with a unique minimum in R^n. We divide the vector of n-variables to d groups of vector subvariables with d at least two. We assume that we can find the partial minimum of f with respect to each vector subvariable while other variables are fixed. We then describe an algorithm that partially minimizes each time on a specifically chosen vector subvariable. This algorithm converges geometrically to the unique minimum. The rate of convergence depends on the uniform bounds on the eigenvalues of the Hessian of f in the compact sublevel set f whose values are at most f(x_0), where x_0 is the starting point of the algorithm. In the case where f is a polynomial of degree two, with positive definite quadratic term, and d=n our method can be considered as a generalization of the classical conjugate gradient method. The main result of this paper is the observation that the celebrated Sinkhorn diagonal scaling algorithm for matrices, and the corresponding diagonal scaling of tensors, can be viewed as partial minimization of certain logconvex functions.