# User:Jarle Pahr/SciPy

Notes on the SciPy Python library:

Testing:

```import scipy as sci
sci.test()
```

# Optimization

For comparison see http://www.mathworks.se/help/optim/ug/fmincon.html

Constrained minimization of multivariate scalar functions (minimize): http://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html#tutorial-sqlsp

## Functions

• General interface to several methods for minimization of multi-variate scalar function.

scipy.optimize.fmin_powell:

• Unconstrained, non-linear optimization using a conjugate gradient algorithm.

Constrained Optimization BY Linear Approximation (COBYLA): http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_cobyla.html#scipy.optimize.fmin_cobyla

• Constrained non-linear optimization with inequality constraints
• Variable bounds and equality constraints not explicitly supported (must be implemented as inequality constraints).
• Reference:

Advances in Optimization and Numerical Analysis Mathematics and Its Applications Volume 275, 1994, pp 51-67 A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation. M. J. D. Powell.

Sequential Least Squares Programming (SLSQP):

scipy.optimize.fmin_ncg:

• Unconstrained optimization by Newton-Conjugate Gradient(NCG) method.

scipy.optimize.fmin_tnc:

• Truncated Newton-CG method. Allows variable bounds. Does not support equality/inequality constraints.
• Unconstrained, non-linear optimization using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm.

scipy.optimize.fmin_l_bfgs_b: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html#scipy.optimize.fmin_l_bfgs_b

Global solvers:

• Simulated annealing. Non-linear optimization. Supports bound constraints.