The scipy.optimize package provides several commonly used optimization algorithms. A detailed listing is available: scipy.optimize (can also be found by help(scipy.optimize)).

It includes solvers for nonlinear problems (with support for both local and global optimization algorithms), linear programming, constrained and nonlinear least-squares, root finding, and curve fitting.

The algorithm is a derivative-free trust-region SQP method based on quadratic approximations to the objective function and each nonlinear constraint. The bounds are treated as unrelaxable constraints, …

Differential evolution is a stochastic population based method that is useful for global optimization problems. At each pass through the population the algorithm mutates each candidate solution by …

This can be used to print or plot the optimization results at each step, and to stop the optimization algorithm based on some user-defined condition. Only implemented for the trf and dogbox methods.

milp is a wrapper of the HiGHS linear optimization software [1]. The algorithm is deterministic, and it typically finds the global optimum of moderately challenging mixed-integer linear programs (when it …

Added in version 1.3.0. Method ‘simplex’ uses a traditional, full-tableau implementation of Dantzig’s simplex algorithm [1], [2] (not the Nelder-Mead simplex). This algorithm is included for backwards …

Safer algorithms are brentq, brenth, ridder, and bisect, but they all require that the root first be bracketed in an interval where the function changes sign. The brentq algorithm is recommended for general use …

It reflects the trust the algorithm puts in the local approximation of the optimization problem. For an accurate local approximation the trust-region should be large and for an approximation valid only …

It should be easy to understand the algorithm just by reading our code. Our code diverges a bit from standard presentations: we choose a different formula for the extrapolation step.