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PEPit: Performance Estimation in Python

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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/PerformanceEstimation/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:

  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},

Demo Open In Colab

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.


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/PerformanceEstimation/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

You can also try the package in this Binder repository. Binder


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=1):
    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`.

    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 3.1]:

    .. math:: f(x_n)-f_\\star \\leqslant \\frac{L}{4nL\\gamma+2} \\|x_0-x_\\star\\|^2,

    which is tight on some Huber loss functions.


    `[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.

        L (float): the smoothness parameter.
        gamma (float): step-size.
        n (int): number of iterations.
        verbose (int): Level of information details to print.

                        - -1: No verbose at all.
                        - 0: This example's output.
                        - 1: This example's output + PEPit information.
                        - 2: This example's output + PEPit information + CVXPY details.

        pepit_tau (float): worst-case value
        theoretical_tau (float): theoretical value

        >>> L = 3
        >>> pepit_tau, theoretical_tau = wc_gradient_descent(L=L, gamma=1 / L, n=4, verbose=1)
        (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: Adding initial conditions and general constraints ...
        (PEPit) Setting up the problem: initial conditions and general constraints (1 constraint(s) added)
        (PEPit) Setting up the problem: interpolation conditions for 1 function(s)
                         function 1 : Adding 30 scalar constraint(s) ...
                         function 1 : 30 scalar constraint(s) added
        (PEPit) Compiling SDP
        (PEPit) Calling SDP solver
        (PEPit) Solver status: optimal (solver: SCS); optimal value: 0.16666664596175398
        *** 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, 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(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(x) - fs)

    # Solve the PEP
    pepit_verbose = max(verbose, 0)
    pepit_tau = problem.solve(verbose=pepit_verbose)

    # Compute theoretical guarantee (for comparison)
    theoretical_tau = L / (2 * (2 * n * L * gamma + 1))

    # Print conclusion if required
    if verbose != -1:
        print('*** Example file: worst-case performance of gradient descent with fixed step-sizes ***')
        print('\tPEPit guarantee:\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=1)

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:


This toolbox has been created by


The authors would like to thank Rémi Flamary for his feedbacks on preliminary versions of the toolbox, as well as for support regarding the continuous integration.


All external contributions are welcome. Please read the contribution guidelines.


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Indices and tables