Implementations of algorithms for solving basic but often encountered optimization problems for educational purposes.
- Python directory: numpy, nptyping, matplotlib are required.
- Available Objectives:
- L-smooth, mu-strongly convex objective.
- Least square objective, || Ax - b ||_2^2.
- Available Algorithms:
- Gradient Descent.
- Accelerated Gradient Descent (Nesterov Accelerated Gradient).
- Stochastic gradient Descent.
- Available Objectives:
- MatrixLS_Sparsity_Constraints: Eigen library is required (C++ code).
Solving a Matrix Least Squares Problem under sparsity constraints.
Available Algorithms:- Alternating Direction Method of Multipliers (ADMM).
- Fast Iterative Shrinkage/Thresholding Algorithm.