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https://github.com/rfsantacruz/cvx-nb
IPython Notebooks of Convex Optimization Problems
https://github.com/rfsantacruz/cvx-nb
convex-optimization cvxpy ipython-notebook optimization python
Last synced: 28 days ago
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IPython Notebooks of Convex Optimization Problems
- Host: GitHub
- URL: https://github.com/rfsantacruz/cvx-nb
- Owner: rfsantacruz
- Created: 2017-04-14T05:54:44.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-11-09T08:34:16.000Z (almost 1 year ago)
- Last Synced: 2024-09-28T23:40:55.898Z (about 1 month ago)
- Topics: convex-optimization, cvxpy, ipython-notebook, optimization, python
- Language: Jupyter Notebook
- Size: 110 KB
- Stars: 13
- Watchers: 2
- Forks: 12
- Open Issues: 1
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# cvx-nb: Convex Optimization Notebooks
This repository contains a set of IPython Notebooks with solution for interesting optimization problems. Some of the problems and data are from the book, [Convex Optimization](http://stanford.edu/~boyd/cvxbook/) by Stephen Boyd and Lieven Vandenberghe.
## Requirements:
The notebooks are developed with:
* Python 3
* Numpy
* SciPy
* Pandas
* CVXOPT
* CVXPY
These libraries can be easily installed using Anaconda and Pip.
## Notebooks
1. [Activity Level Problem](./OptimalActivityLevel.ipynb): Solution to a trivial economic activity level problem.
2. [Illumination Problem](./Illumination.ipynb): Approximate and exact solutions for a toy example about how to choose the bounded power of lamps to illuminate a indoor space.
3. [Doubly Stochastic Approximation](./DoublyStochasticApproximation.ipynb): How to find the closest doubly stochastic matrix from a given arbitrary matrix.
4. [Complex Least Norm](./ComplexLeastNorm.ipynb): The classical least norm problem in the complex domain.
5. [Minimum Fuel Optimal Control](./MinimumFuelOptimalControl.ipynb): Minimization of fuel consuption in a simple dynamic linear system.
6. [Portfolio Optimization](./SimplePortfolioOptimization.ipynb): Risk-minimization and risk-return trade-off curves on the classical portfolio optimization problem.