Welcome to PEPit’s documentation!
PEPit: Performance Estimation in Python
This open source Python library provides a generic way to use PEP framework in Python. Performance estimation problems were introduced in 2014 by Yoel Drori and Marc Teboulle, see [1]. PEPit is mainly based on the formalism and developments from [2, 3] by a subset of the authors of this toolbox. A friendly informal introduction to this formalism is available in this blog post and a corresponding Matlab library is presented in [4] (PESTO).
Website and documentation of PEPit: https://pepit.readthedocs.io/
Source Code (MIT): https://github.com/bgoujaud/PEPit
Using and citing the toolbox
This code comes jointly with the following reference
:
B. Goujaud, C. Moucer, F. Glineur, J. Hendrickx, A. Taylor, A. Dieuleveut (2022).
"PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python."
When using the toolbox in a project, please refer to this note via this Bibtex entry:
@article{pepit2022,
title={{PEPit}: computer-assisted worst-case analyses of first-order optimization methods in {P}ython},
author={Goujaud, Baptiste and Moucer, C\'eline and Glineur, Fran\c{c}ois and Hendrickx, Julien and Taylor, Adrien and Dieuleveut, Aymeric},
journal={arXiv preprint arXiv:2201.04040},
year={2022}
}
Demo
This notebook provides a demonstration of how to use PEPit to obtain a worst-case guarantee on a simple algorithm (gradient descent), and a more advanced analysis of three other examples.
Installation
The library has been tested on Linux and MacOSX. It relies on the following Python modules:
Numpy
Scipy
Cvxpy
Matplotlib (for the demo notebook)
Pip installation
You can install the toolbox through PyPI with:
pip install pepit
or get the very latest version by running:
pip install -U https://github.com/bgoujaud/PEPit/archive/master.zip # with --user for user install (no root)
Post installation check
After a correct installation, you should be able to import the module without errors:
import PEPit
Online environment
Example
The folder Examples contains numerous introductory examples to the toolbox.
Among the other examples, the following code (see GradientMethod
)
generates a worst-case scenario for iterations of the gradient method, applied to the minimization of a smooth (possibly strongly) convex function f(x).
More precisely, this code snippet allows computing the worst-case value of when is generated by gradient descent, and when .
from PEPit import PEP
from PEPit.functions import SmoothStronglyConvexFunction
def wc_gradient_descent(L, gamma, n, verbose=True):
"""
Consider the convex minimization problem
.. math:: f_\\star \\triangleq \\min_x f(x),
where :math:`f` is :math:`L`-smooth and convex.
This code computes a worst-case guarantee for **gradient descent** with fixed step-size :math:`\\gamma`.
That is, it computes the smallest possible :math:`\\tau(n, L, \\gamma)` such that the guarantee
.. math:: f(x_n) - f_\\star \\leqslant \\tau(n, L, \\gamma) || x_0 - x_\\star ||^2
is valid, where :math:`x_n` is the output of gradient descent with fixed step-size :math:`\\gamma`, and
where :math:`x_\\star` is a minimizer of :math:`f`.
In short, for given values of :math:`n`, :math:`L`, and :math:`\\gamma`, :math:`\\tau(n, L, \\gamma)` is computed as the worst-case
value of :math:`f(x_n)-f_\\star` when :math:`||x_0 - x_\\star||^2 \\leqslant 1`.
**Algorithm**:
Gradient descent is described by
.. math:: x_{t+1} = x_t - \\gamma \\nabla f(x_t),
where :math:`\\gamma` is a step-size.
**Theoretical guarantee**:
When :math:`\\gamma \\leqslant \\frac{1}{L}`, the **tight** theoretical guarantee can be found in [1, Theorem 1]:
.. math:: f(x_n)-f_\\star \\leqslant \\frac{L||x_0-x_\\star||^2}{4nL\\gamma+2},
which is tight on some Huber loss functions.
**References**:
`[1] Y. Drori, M. Teboulle (2014). Performance of first-order methods for smooth convex minimization: a novel
approach. Mathematical Programming 145(1–2), 451–482.
<https://arxiv.org/pdf/1206.3209.pdf>`_
Args:
L (float): the smoothness parameter.
gamma (float): step-size.
n (int): number of iterations.
verbose (bool): if True, print conclusion
Returns:
pepit_tau (float): worst-case value
theoretical_tau (float): theoretical value
Example:
>>> L = 3
>>> pepit_tau, theoretical_tau = wc_gradient_descent(L=L, gamma=1 / L, n=4, verbose=True)
(PEPit) Setting up the problem: size of the main PSD matrix: 7x7
(PEPit) Setting up the problem: performance measure is minimum of 1 element(s)
(PEPit) Setting up the problem: initial conditions (1 constraint(s) added)
(PEPit) Setting up the problem: interpolation conditions for 1 function(s)
function 1 : 30 constraint(s) added
(PEPit) Compiling SDP
(PEPit) Calling SDP solver
(PEPit) Solver status: optimal (solver: MOSEK); optimal value: 0.16666666497937685
*** Example file: worst-case performance of gradient descent with fixed step-sizes ***
PEPit guarantee: f(x_n)-f_* <= 0.166667 ||x_0 - x_*||^2
Theoretical guarantee: f(x_n)-f_* <= 0.166667 ||x_0 - x_*||^2
"""
# Instantiate PEP
problem = PEP()
# Declare a strongly convex smooth function
func = problem.declare_function(SmoothStronglyConvexFunction, param={'mu': 0, 'L': L})
# Start by defining its unique optimal point xs = x_* and corresponding function value fs = f_*
xs = func.stationary_point()
fs = func.value(xs)
# Then define the starting point x0 of the algorithm
x0 = problem.set_initial_point()
# Set the initial constraint that is the distance between x0 and x^*
problem.set_initial_condition((x0 - xs) ** 2 <= 1)
# Run n steps of the GD method
x = x0
for _ in range(n):
x = x - gamma * func.gradient(x)
# Set the performance metric to the function values accuracy
problem.set_performance_metric(func.value(x) - fs)
# Solve the PEP
pepit_tau = problem.solve(verbose=verbose)
# Compute theoretical guarantee (for comparison)
theoretical_tau = L / (2 * (2 * n * L * gamma + 1))
# Print conclusion if required
if verbose:
print('*** Example file: worst-case performance of gradient descent with fixed step-sizes ***')
print('\tPEPit guarantee:\t\t f(x_n)-f_* <= {:.6} ||x_0 - x_*||^2'.format(pepit_tau))
print('\tTheoretical guarantee:\t f(x_n)-f_* <= {:.6} ||x_0 - x_*||^2'.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, verbose=True)
Included tools
A lot of common optimization methods can be studied through this framework, using numerous steps and under a large variety of function / operator classes.
PEPit provides the following steps (often referred to as “oracles”):
PEPit provides the following function classes CNIs:
PEPit provides the following operator classes CNIs:
Contributions
All external contributions are welcome. Please read the contribution guidelines.
References
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