Strongly monotone and Lipschitz continuous.PEPit provides the following operator classes CNIs: Functions verifying restricted secant inequality and upper error bound.

PEPit provides the following function classes CNIs: PEPit provides the following steps (often referred to as 'oracles'): Using numerous steps and under a large variety of function / operator classes. format ( theoretical_tau )) # Return the worst-case guarantee of the evaluated method (and the reference theoretical value) return pepit_tau, theoretical_tau if _name_ = '_main_' : L = 3 pepit_tau, theoretical_tau = wc_gradient_descent ( L = L, gamma = 1 / L, n = 4, wrapper = 'cvxpy', solver = None, verbose = 1 ) Included toolsĪ lot of common optimization methods can be studied through this framework, When using the toolbox in a project, please refer to this note via this Bibtex entry: ||x_0 - x_*||^2'. 'PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python.' This code comes jointly with the following reference: B. Source Code (MIT): Using and citing the toolbox PEPit is mainly based on the formalism and developments from by a subset of the authors of this toolbox.Ī friendly informal introduction to this formalism is available in this blog postĪnd a corresponding Matlab library is presented in ( PESTO). Performance estimation problems were introduced in 2014 by Yoel Drori and Marc Teboulle, see.

This open source Python library provides a generic way to use PEP framework in Python.

Optimizer 16.3 instaling