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pro vyhledávání: '"Taylor, Adrien B."'
We present a methodology for establishing the existence of quadratic Lyapunov inequalities for a wide range of first-order methods used to solve convex optimization problems. In particular, we consider (i) classes of optimization problems of finite-s
Externí odkaz:
http://arxiv.org/abs/2302.06713
Operator Splitting Performance Estimation: Tight contraction factors and optimal parameter selection
We propose a methodology for studying the performance of common splitting methods through semidefinite programming. We prove tightness of the methodology and demonstrate its value by presenting two applications of it. First, we use the methodology as
Externí odkaz:
http://arxiv.org/abs/1812.00146
Akademický článek
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Autor:
Drori, Yoel, Taylor, Adrien B.
We describe a novel constructive technique for devising efficient first-order methods for a wide range of large-scale convex minimization settings, including smooth, non-smooth, and strongly convex minimization. The technique builds upon a certain va
Externí odkaz:
http://arxiv.org/abs/1803.05676
Autor:
Barré, Mathieu1 (AUTHOR) mathieu.barre83@gmail.com, Taylor, Adrien B.2 (AUTHOR), Bach, Francis2 (AUTHOR)
Publikováno v:
Mathematical Programming. Sep2023, Vol. 201 Issue 1/2, p185-230. 46p.
Exact worst-case convergence rates of the proximal gradient method for composite convex minimization
We study the worst-case convergence rates of the proximal gradient method for minimizing the sum of a smooth strongly convex function and a non-smooth convex function whose proximal operator is available. We establish the exact worst-case convergence
Externí odkaz:
http://arxiv.org/abs/1705.04398
We consider the gradient (or steepest) descent method with exact line search applied to a strongly convex function with Lipschitz continuous gradient. We establish the exact worst-case rate of convergence of this scheme, and show that this worst-case
Externí odkaz:
http://arxiv.org/abs/1606.09365
Publikováno v:
SIAM Journal on Optimization, 27(3), 1283-1313 (2017)
We provide a framework for computing the exact worst-case performance of any algorithm belonging to a broad class of oracle-based first-order methods for composite convex optimization, including those performing explicit, projected, proximal, conditi
Externí odkaz:
http://arxiv.org/abs/1512.07516
We show that the exact worst-case performance of fixed-step first-order methods for unconstrained optimization of smooth (possibly strongly) convex functions can be obtained by solving convex programs. Finding the worst-case performance of a black-bo
Externí odkaz:
http://arxiv.org/abs/1502.05666
Akademický článek
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