cvxgrp / cvx_short_course

Materials for a short course on convex optimization.

CVXPY tutorial

CVXPY is an open source Python-embedded modeling language for convex optimization problems. It lets you express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers. This tutorial will cover the basics of convex optimization, and how to use CVXPY to specify and solve convex optimization problems, with an emphasis on real-world applications. No prior knowledge of convex optimization is assumed.

Getting started

1. Clone this repository.
2. Install CVXPY following these instructions. We recommend Python 3.10 and CVXPY 1.3.
3. Test your CVXPY installation by running test.py in the repository.

cvx_short_course's Issues

introduce github workflow

Comments on local execution

Issue on page /docs/exercises/notebooks/13.21.html

rendering

Issue on page /docs/index.html

Your issue content here.

Rename master branch to main branch

Let's just follow the name standards...

Issue on page /docs/applications/notebooks/portfolio_optimization.html

Rendering

Issue on page /docs/intro/notebooks/control.html

Latex rendering off

Create a codespace

Issue on page /docs/local.html

spelling mistake from within

Radiation treatment example

@SteveDiamond wrote about the slow inpaiting example:

This example is not impressive anymore given all that's happened in computer vision. We should drop it. When I give the talk I use a radiation treatment planning example instead.

Please add the example

Create make file to support local builds of the book

Cache Binder builds

Iterate through notebooks and check header

Revisit all notebooks and check for LaTeX problems etc.

Add missing license file

Issue on page /docs/intro/notebooks/tv_inpainting.html

execution too slow

Revisit README

Most of it can go as baked into the book

Revisit workflows

Introduce sections

revisit pre-commit

Move applications into book and revisit display

Issue on page /docs/exercises/notebooks/hello_world.html

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Revisit Stephen Boyd's webpage

I think we could slim it down by removing the links that point to individual chapters/section in the Jupyter Book.

Issue on page /docs/exercises/notebooks/Lasso.html

rendering

Issue on page /docs/applications/notebooks/portfolio_optimization.html

too slow and not correct rendering

testing a new branch

move binder.yml

Sigma is not positive semi-definite in Simple portfolio optimization

Hi! First of all, I apologize if this is something that was already taken into account or not the appropriate place to comment this.

I came across this repo in Boyd's website and decided to follow the exercises. Then, when I tried to optimize the problem an error occurred saying the objective wasn't DCP. After a while, comparing with Boyd's notebook, I noticed that Sigma wasn't being positive semi-definite, and thus not convex.

Does this make sense or is it just due to the randomness when creating the values?

Remove expensive binder caching

Binder link

Issue on page /docs/exercises/notebooks/13.22.html

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Issue on page /docs/exercises/notebooks/13.20.html

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