Derivative free methodologies for circuit worst case analysis

Latorre, Vittorio; Habal, Husni; Graeb, Helmut; Lucidi, Stefano

doi:10.1007/s11590-018-1364-5

Cited by 2 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ciccazzo et al. (2015) and Latorre, Habal, Graeb and Lucidi (2019) develop derivative-free methods that employ approximate solutions of the inner problem in (5.14). Menickelly and Wild (2019) also consider (5.14) and develop a derivative-free method of outer approximations for more general .…”

Section: Methods For Structured Objectivesmentioning

confidence: 99%

“…The authors assume that approximate gradients of f l are available and propose methods with convergence analysis restricted to functions f in (63) that are convex in x. Ciccazzo et al [2015] and Latorre et al [2019] develop derivative-free methods that employ approximate solutions of the inner problem in (63). Menickelly and Wild [2019] also consider ( 63) and develop a derivative-free method of outer approximations for more general f .…”

Section: Bilevel and General Minimax Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Derivative-free optimization methods

2019

View full text Add to dashboard Cite

Dedicated to the memory of Andrew R. Conn for his inspiring enthusiasm and his many contributions to the renaissance of derivative-free optimization methods. AbstractIn many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of a black-box or simulation oracle that does not provide derivative information. Such settings necessitate the use of methods for derivative-free, or zeroth-order, optimization. We provide a review and perspectives on developments in these methods, with an emphasis on highlighting recent developments and on unifying treatment of such problems in the non-linear optimization and machine learning literature. We categorize methods based on assumed properties of the black-box functions, as well as features of the methods. We first overview the primary setting of deterministic methods applied to unconstrained, non-convex optimization problems where the objective function is defined by a deterministic black-box oracle. We then discuss developments in randomized methods, methods that assume some additional structure about the objective (including convexity, separability and general non-smooth compositions), methods for problems where the output of the black-box oracle is stochastic, and methods for handling different types of constraints.

show abstract