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Title: GitHub - PerformanceEstimation/PEPit: PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

Open Graph Title: GitHub - PerformanceEstimation/PEPit: PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

X Title: GitHub - PerformanceEstimation/PEPit: PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

Description: PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods. - PerformanceEstimation/PEPit

Open Graph Description: PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods. - PerformanceEstimation/PEPit

X Description: PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods. - PerformanceEstimation/PEPit

Opengraph URL: https://github.com/PerformanceEstimation/PEPit

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https://pypi.python.org/pypi/PEPit/
https://pepy.tech/project/pepit
https://github.com/PerformanceEstimation/PEPit/blob/master/LICENSE
blog posthttps://francisbach.com/computer-aided-analyses/
PESTOhttps://github.com/AdrienTaylor/Performance-Estimation-Toolbox
https://pepit.readthedocs.io/https://pepit.readthedocs.io/en/latest/
https://github.com/PerformanceEstimation/PEPithttps://github.com/PerformanceEstimation/PEPit
https://patch-diff.githubusercontent.com/PerformanceEstimation/PEPit#using-and-citing-the-toolbox
referencehttps://arxiv.org/pdf/2201.04040.pdf
https://colab.research.google.com/github/PerformanceEstimation/PEPit/blob/master/ressources/demo/PEPit_demo.ipynb
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stepshttps://pepit.readthedocs.io/en/latest/api/steps.html
Inexact gradient stephttps://pepit.readthedocs.io/en/latest/api/steps.html#inexact-gradient-step
Exact line-search stephttps://pepit.readthedocs.io/en/latest/api/steps.html#exact-line-search-step
Proximal stephttps://pepit.readthedocs.io/en/latest/api/steps.html#proximal-step
Inexact proximal stephttps://pepit.readthedocs.io/en/latest/api/steps.html#inexact-proximal-step
Bregman gradient stephttps://pepit.readthedocs.io/en/latest/api/steps.html#bregman-gradient-step
Bregman proximal stephttps://pepit.readthedocs.io/en/latest/api/steps.html#bregman-proximal-step
Linear optimization stephttps://pepit.readthedocs.io/en/latest/api/steps.html#linear-optimization-step
Linearly shifted optimization stephttps://pepit.readthedocs.io/en/latest/api/steps.html#linearly-shifted-optimization-step
Epsilon-subgradient stephttps://pepit.readthedocs.io/en/latest/api/steps.html#epsilon-subgradient-step
function classeshttps://pepit.readthedocs.io/en/latest/api/functions.html
Convexhttps://pepit.readthedocs.io/en/latest/api/functions.html#convex
Strongly convexhttps://pepit.readthedocs.io/en/latest/api/functions.html#strongly-convex
Smoothhttps://pepit.readthedocs.io/en/latest/api/functions.html#smooth
Convex and smoothhttps://pepit.readthedocs.io/en/latest/api/functions.html#convex-and-smooth
Convex and block-smoothhttps://pepit.readthedocs.io/en/latest/api/functions.htmlconvex-and-smooth-by-block
stronger varianthttps://pepit.readthedocs.io/en/latest/api/functions.html#convex-and-smooth-by-block-refined-expensive-version
Strongly convex and smoothhttps://pepit.readthedocs.io/en/latest/api/functions.html#strongly-convex-and-smooth
Convex and Lipschitz continuoushttps://pepit.readthedocs.io/en/latest/api/functions.html#convex-and-lipschitz-continuous
Convex indicatorhttps://pepit.readthedocs.io/en/latest/api/functions.html#convex-indicator
Convex supporthttps://pepit.readthedocs.io/en/latest/api/functions.html#convex-support-functions
Convex quadratically growinghttps://pepit.readthedocs.io/en/latest/api/functions.html#convex-and-quadratically-upper-bounded
Functions verifying restricted secant inequality and upper error boundhttps://pepit.readthedocs.io/en/latest/api/functions.html#restricted-secant-inequality-and-error-bound
Smooth function satisfying a quadratic Lojasiewicz inequalityhttps://pepit.readthedocs.io/en/latest/api/functions.html#smooth-function-satisfying-quadratic-lojasiewicz-inequality-cheap-version
stronger varianthttps://pepit.readthedocs.io/en/latest/api/functions.html#smooth-function-satisfying-quadratic-lojasiewicz-inequality-expensive-version
operator classeshttps://pepit.readthedocs.io/en/latest/api/operators.html
Monotonehttps://pepit.readthedocs.io/en/latest/api/operators.html#monotone
Strongly monotonehttps://pepit.readthedocs.io/en/latest/api/operators.html#strongly-monotone
Lipschitz continuoushttps://pepit.readthedocs.io/en/latest/api/operators.html#lipschitz-continuous
Strongly monotone and Lipschitz continuoushttps://pepit.readthedocs.io/en/latest/api/operators.html#strongly-monotone-and-lipschitz-continuous-cheap-version
stronger varianthttps://pepit.readthedocs.io/en/latest/api/functions.html#strongly-monotone-and-lipschitz-continuous-expensive-version
Cocoercivehttps://pepit.readthedocs.io/en/latest/api/operators.html#cocoercive
Strongly monotone and cocoercivehttps://pepit.readthedocs.io/en/latest/api/operators.html#cocoercive-and-strongly-monotone-cheap-version
stronger varianthttps://pepit.readthedocs.io/en/latest/api/operators.html#cocoercive-and-strongly-monotone-expensive-version
https://patch-diff.githubusercontent.com/PerformanceEstimation/PEPit#contributors
https://patch-diff.githubusercontent.com/PerformanceEstimation/PEPit#creators
Baptiste Goujaudhttps://www.linkedin.com/in/baptiste-goujaud-b60060b3/
Céline Moucerhttps://cmoucer.github.io
Julien Hendrickxhttps://perso.uclouvain.be/julien.hendrickx/index.html
François Glineurhttps://perso.uclouvain.be/francois.glineur/
Adrien Taylorhttps://adrientaylor.github.io/
Aymeric Dieuleveuthttp://www.cmap.polytechnique.fr/~aymeric.dieuleveut/
https://www.inria.fr
https://www.polytechnique.edu/
https://www.uclouvain.be/
https://patch-diff.githubusercontent.com/PerformanceEstimation/PEPit#external-contributions
contribution guidelineshttps://pepit.readthedocs.io/en/latest/contributing.html
Gyumin Rohhttps://rkm0959.tistory.com/
Jisun Parkhttps://jisunp515.github.io
Nizar Bousselmihttps://nizarbousselmi.github.io
Henry Shugarthttps://statistics.wharton.upenn.edu/profile/hshugart/
Julien Weibelhttps://www.normalesup.org/~jweibel/
Pierre Gaillardhttp://pierre.gaillard.me/
Wouter Koolenhttps://wouterkoolen.info/
Anne Rubbenshttps://www.uclouvain.be/fr/people/anne.rubbens
https://patch-diff.githubusercontent.com/PerformanceEstimation/PEPit#acknowledgments
Rémi Flamaryhttps://remi.flamary.com/
https://patch-diff.githubusercontent.com/PerformanceEstimation/PEPit#funding
https://erc.europa.eu/homepage
https://anr.fr/
https://patch-diff.githubusercontent.com/PerformanceEstimation/PEPit#references
Performance of first-order methods for smooth convex minimization: a novel approachhttps://arxiv.org/pdf/1206.3209.pdf
Smooth strongly convex interpolation and exact worst-case performance of first-order methodshttps://arxiv.org/pdf/1502.05666.pdf
Exact worst-case performance of first-order methods for composite convex optimizationhttps://arxiv.org/pdf/1512.07516.pdf
Performance Estimation Toolbox (PESTO): automated worst-case analysis of first-order optimization methodshttps://adrientaylor.github.io/share/PESTO_CDC_2017.pdf
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Pythonhttps://arxiv.org/pdf/2201.04040
Monotone operators and the proximal point algorithmhttps://epubs.siam.org/doi/pdf/10.1137/0314056
An accelerated hybrid proximal extragradient method for convex optimization and its implications to second-order methodshttps://epubs.siam.org/doi/abs/10.1137/110833786
Inexact and accelerated proximal point algorithmshttp://www.optimization-online.org/DB_FILE/2011/08/3128.pdf
Principled analyses and design of first-order methods with inexact proximal operatorshttps://arxiv.org/pdf/2006.06041v3.pdf
Acceleration Methodshttps://arxiv.org/pdf/2101.09545.pdf
DC programming and DCA: thirty years of developmentshttps://link.springer.com/article/10.1007/s10107-018-1235-y
Efficient first-order methods for convex minimization: a constructive approachhttps://arxiv.org/pdf/1803.05676.pdf
Worst-case convergence analysis of inexact gradient and Newton methods through semidefinite programming performance estimationhttps://arxiv.org/pdf/1709.05191.pdf
An algorithm for quadratic programminghttps://arxiv.org/pdf/1608.04826.pdf
Relative-error approximate versions of Douglas–Rachford splitting and special cases of the ADMMhttps://link.springer.com/article/10.1007/s10107-017-1160-5
Principled analyses and design of first-order methods with inexact proximal operators, arXiv 2006https://arxiv.org/pdf/2006.06041v2.pdf
A note on approximate accelerated forward-backward methods with absolute and relative errors, and possibly strongly convex objectiveshttps://arxiv.org/pdf/2106.15536v2.pdf
Complexity guarantees for Polyak steps with momentumhttps://arxiv.org/pdf/2002.00915.pdf
Proximal minimization algorithm with D-functionshttps://link.springer.com/content/pdf/10.1007/BF00940051.pdf
A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applicationshttps://cmps-people.ok.ubc.ca/bauschke/Research/103.pdf
Optimal complexity and certification of Bregman first-order methodshttps://arxiv.org/pdf/1911.08510.pdf
A three-operator splitting scheme and its optimization applicationshttps://arxiv.org/pdf/1504.01032.pdf
Exact worst-case convergence rates of the proximal gradient method for composite convex minimizationhttps://arxiv.org/pdf/1705.04398.pdf
Introduction to Optimizationhttps://www.researchgate.net/profile/Boris-Polyak-2/publication/342978480_Introduction_to_Optimization/links/5f1033e5299bf1e548ba4636/Introduction-to-Optimization.pdf
A primer on monotone operator methodshttps://web.stanford.edu/~boyd/papers/pdf/monotone_primer.pdf
Analysis and design of optimization algorithms via integral quadratic constraintshttps://arxiv.org/pdf/1408.3595.pdf
Revisiting Frank-Wolfe: Projection-free sparse convex optimizationhttp://proceedings.mlr.press/v28/jaggi13.pdf
Douglas-Rachford splitting: Complexity estimates and accelerated variantshttps://arxiv.org/pdf/1407.6723.pdf
Interior gradient and proximal methods for convex and conic optimizationhttps://epubs.siam.org/doi/pdf/10.1137/S1052623403427823
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problemshttps://www.ceremade.dauphine.fr/~carlier/FISTA
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A Lipschitz condition preserving extension for a vector functionhttps://www.jstor.org/stable/2371917
Convex Analysis and Monotone Operator Theory in Hilbert Spaceshttps://link.springer.com/book/10.1007/978-3-319-48311-5
Convergence of proximal point and extragradient-based methods beyond monotonicity: the case of negative comonotonicityhttps://proceedings.mlr.press/v202/gorbunov23a/gorbunov23a.pdf
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