5 papers · 1 filter
Conditions for linear convergence of the gradient method for non-convex optimization
Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani
In this paper, we derive a new linear convergence rate for the gradient method with fixed step lengths for non-convex smooth optimization problems satisfying the Polyak-Lojasiewicz…
The exact worst-case convergence rate of the gradient method with fixed step lengths for L-smooth functions
Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani
In this paper, we study the convergence rate of the gradient (or steepest descent) method with fixed step lengths for finding a stationary point of an -smooth function. We estab…
Convergence analysis of a Lasserre hierarchy of upper bounds for polynomial minimization on the sphere
Etienne de Klerk, Monique Laurent
We study the convergence rate of a hierarchy of upper bounds for polynomial minimization problems, proposed by Lasserre [SIAM J. Optim. 21(3) (2011), pp. 864-885], for the special…
A survey of semidefinite programming approaches to the generalized problem of moments and their error analysis
Etienne de Klerk, Monique Laurent
The generalized problem of moments is a conic linear optimization problem over the convex cone of positive Borel measures with given support. It has a large variety of applications…
Distributionally robust optimization with polynomial densities: theory, models and algorithms
Etienne de Klerk, Daniel Kuhn, Krzysztof Postek
In distributionally robust optimization the probability distribution of the uncertain problem parameters is itself uncertain, and a fictitious adversary, e.g., nature, chooses the…