2 citations · 4 across the 5 of their papers we have counts for
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Quadratic minimization: from conjugate gradient to an adaptive Heavy-ball method with Polyak step-sizes
Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut
In this work, we propose an adaptive variation on the classical Heavy-ball method for convex quadratic minimization. The adaptivity crucially relies on so-called "Polyak step-sizes…
Optimal first-order methods for convex functions with a quadratic upper bound
Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut
We analyze worst-case convergence guarantees of first-order optimization methods over a function class extending that of smooth and convex functions. This class contains convex fun…
A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip
Mathieu Even, Raphaël Berthier, Francis Bach +5
We introduce the continuized Nesterov acceleration, a close variant of Nesterov acceleration whose variables are indexed by a continuous time parameter. The two variables continuou…
On the oracle complexity of smooth strongly convex minimization
Yoel Drori, Adrien Taylor
We construct a family of functions suitable for establishing lower bounds on the oracle complexity of first-order minimization of smooth strongly-convex functions. Based on this co…
Complexity Guarantees for Polyak Steps with Momentum
Mathieu Barré, Adrien Taylor, Alexandre d'Aspremont
In smooth strongly convex optimization, knowledge of the strong convexity parameter is critical for obtaining simple methods with accelerated rates. In this work, we study a class…
Optimal Complexity and Certification of Bregman First-Order Methods
Radu-Alexandru Dragomir, Adrien Taylor, Alexandre d'Aspremont +1
We provide a lower bound showing that the convergence rate of the NoLips method (a.k.a. Bregman Gradient) is optimal for the class of functions satisfying the -smoothne…