Python Scipy

Fundamental algorithms for scientific computing in Python: Scipy 

SciPy User Guide 

Scipy Optimize 

Scipy minimize: algorithms 

scipy.optimize.least_squares: lsq 

Optimization and root finding: scipy 

Three examples of nonlinear least-squares fitting in Python with SciPy: examples 

Scipy Lecture Notes: notes 

Python scipy.optimize.leastsq() Examples 

minimize(method=’BFGS’): BFGS 

Broyden–Fletcher–Goldfarb–Shanno algorithm: wikipedia 

Limited-memory BFGS: wikipedia 

Large-scale Bound-constrained Optimization: L-BFGS-B

Newton’s method in optimization: wikipedia 

Conjugate gradient method: wikipedia 

minimize(method=’Newton-CG’): Scipy 

A Gentle Introduction to the BFGS Optimization Algorithm: Jason Brownlee

Curve fitting using scipy and lmfit: lmfit 

minimize(method=’Nelder-Mead’): NM 

How to Use Nelder-Mead Optimization in Python: Jason Brownlee

Nelder–Mead method: wikipedia 

Truncated Newton method: wikipedia 

minimize(method=’TNC’): Scipy 

minimize(method=’CG’): Scipy 

Conjugate gradient method: wikipedia

minimize(method=’COBYLA’): COBYLA 

Constrained optimization by linear approximation (COBYLA) method: wikipedia 

minimize(method=’SLSQP’): Scipy 

Sequential quadratic programming: wikipedia 

minimize(method=’Powell’): Scipy 

Powell’s method: wikipedia 

A view of algorithms for optimization without derivatives – M.J.D. Powel 

Some noninear optimization methods – by Jos van Trier  

Function minimization without evaluating derivatives—a review By R. Fletcher

Trust region: wikipedia 

minimize(method=’trust-constr’): Scipy 

Powell’s dog leg method: wikipedia 

minimize(method=’dogleg’): Scipy 

minimize(method=’trust-ncg’): Scipy 

A review of trust region algorithms for optimization – paper Ya-xiang Yuan

ITERATIVE METHODS FOR FINDING A TRUST-REGION STEP – paper Jennifer B. ERWAY

Trust Region Methods for Unconstrained Optimisation – Lecture Hauser 

Trust-region methods: Wenhe (Wayne) Ye 

minimize(method=’trust-exact’): Scipy 

minimize(method=’trust-krylov’): Scipy 

scipy.optimize.OptimizeResult: Result 

ARTIFICIAL NEURAL NETWORK (ANN) 6 – TRAINING VIA BFGS

LBFGS and OWL-QN optimization algorithms: PyLBFGS 

Getting started with Non-Linear Least-Squares Fitting: LMFIT 

Getting started with Non-Linear Least-Squares Fitting: LMFIT-pdf 

Downloading and Installation: LMFIT 

Modeling Data and Curve Fitting: LMFIT