chapter09_numopt.md

9. Numerical optimization

Full list of references in Chapter 9 of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python, by Dr. Cyrille Rossant, Packt Publishing, 400 pages, 2014.

Recipe 1

• Documentation of scipy.optimize.
• A course on root finding with SciPy.
• The Bisection method.
• The intermediate value theorem.
• Brent's method.
• Newton's method.

Recipe 2

• SciPy.optimize reference documentation.
• An excellent lecture on mathematical optimization with SciPy.
• Definition of the gradient.
• Newton's method.
• Quasi-Newton methods.
• Simulated annealing.
• Metaheuristics for function minimization.
• The CMA-ES algorithm.
• A Python implementation of CMA-ES.

Recipe 3

• Reference documentation of curvefit.
• Non-linear least squares.
• Levenberg-Marquardt algorithm.

Recipe 4

• Potential energy.
• Elastic potential energy.
• Hooke's law, which is the linear approximation of the spring's response.
• Principle of minimum energy.