From de3ab66772ddacbad37595da7bf52093266f173d Mon Sep 17 00:00:00 2001 From: Ellis Brown Date: Mon, 19 Jul 2021 11:44:11 -0700 Subject: [PATCH] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 1f8d9de..001886b 100644 --- a/README.md +++ b/README.md @@ -3,8 +3,8 @@ [![Build status](https://github.com/JuliaFirstOrder/SeparableOptimization.jl/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/JuliaFirstOrder/SeparableOptimization.jl/actions?query=workflow%3ACI+branch%3Amain) [![codecov](https://codecov.io/gh/JuliaFirstOrder/SeparableOptimization.jl/branch/main/graph/badge.svg?token=Cz8LGxvzwx)](https://codecov.io/gh/JuliaFirstOrder/SeparableOptimization.jl) -[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliafirstorder.github.io/SeparableOptimization.jl/stable) -[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliafirstorder.github.io/SeparableOptimization.jl/dev) +[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliafirstorder.github.io/SeparableOptimization.jl/stable/) +[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliafirstorder.github.io/SeparableOptimization.jl/dev/) **SeparableOptimization.jl** is a [Julia](http://julialang.org) package that solves Linearly Constrained Separable Optimization Problems. @@ -25,7 +25,7 @@ where: * `x`, the decision variable, is an `n`-vector. * `g_i` is a piecewise quadratic function, specified via [PiecewiseQuadratics.jl](https://github.com/JuliaFirstOrder/PiecewiseQuadratics.jl). -The algorithm used is the alternating direction method of multipliers (ADMM). This method reaches moderate accuracy very quickly, but often requires some tuning, which may need to be done by hand. This package is therefore best used by someone looking to solve a family of similar optimization problems with excellent performance, even when the function $g_i$ is very complicated. +The algorithm used is the alternating direction method of multipliers (ADMM). This method reaches moderate accuracy very quickly, but often requires some tuning, which may need to be done by hand. This package is therefore best used by someone looking to solve a family of similar optimization problems with excellent performance, even when the function `g_i` is very complicated. ### Authors This package and [PiecewiseQuadratics.jl](https://github.com/JuliaFirstOrder/PiecewiseQuadratics.jl) were originally developed by [Nicholas Moehle](https://www.nicholasmoehle.com/), [Ellis Brown](http://ellisbrown.github.io), and [Mykel Kochenderfer](https://mykel.kochenderfer.com/) at BlackRock AI Labs. They were developed to produce the results in the following paper: [arXiv:2103.05455](https://arxiv.org/abs/2103.05455).