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IPython Notebooks of Convex Optimization Problems

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# 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.

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