Some of the algorithms of the book Algorithms for optmization from Michael Kochenderfer transcribed to Python

Algorithms implemented until now:

1- Adaptative Simulated Annealing
2 - Augmented Lagrange
3 - BFGS
4 - Bracket Line search ( Search Algorithm)
5 - Bracket search
6 - Covariance Matrix Adaptation
7 - Conjugate Gradient
8 - Cross Entropy Method
9 - Cyclic Coordinates descent
10 - Differential Evolution
11 - Firefly Search
12 - Gauss Newton
13 - Genetic Algorithms
14 - Gradient/Hessians calculator
15 - Golden_Search
16 - Gradient Descent
17 - Gradient Descent with momentum methods
18 - Hooke Jeeevs
19 - Interior Point Method
20 - Levenberg Marquadt
21 - MADS
22 - Matthew Daves algorithm for positive definite matrices
23 - Natural evolution strategies
24 - Nelder-Mead Simplex
25 - Newton Method
26 - Particle Swarm Optmization
27 - Penalty Method
28 - Powell method(CG)
29 - Stong Backtracking
... and a decorator to check the performance

To check the algorithms, one should use the Test Functions