This paper is an attempt at describing the State of the Art of the vast field of continuous optimization. We will survey deterministic and stochastic methods as well as hybrid approaches in their application to single objective and multiobjective optimization. We study the parameters of optimization algorithms and possibilities for tuning them. Finally, we discuss several methods for using approximate models for computationally expensive problems
In this dissertation, a theoretical framework based on concentration inequalities for empirical proc...
where the function f or its gradient rf are not directly accessible except through Monte Carlo estim...
In this thesis numerical optimization methods for single- and multi-objective design optimization wi...
Paaßen B, Artelt A, Hammer B. Lecture Notes on Applied Optimization. Faculty of Technology, Bielefel...
This book is about learning for problem solving. [...] Human problem solving is strongly connected t...
This document provides a global view on my research on mathematical optimization. Over the last 10 y...
Model-based optimization methods are a class of random search methods that are useful for solving gl...
Continuous optimization is never easy: the exact solution is always a luxury demand and ...
Masteroppgave i informasjons- og kommunikasjonsteknologi IKT590 2013 – Universitetet i Agder, Grims...
Optimisation problems are of prime importance in scientific and engineering communities. Many day-t...
Machine learning has been a topic in academia and industry for decades. Performance of machine lear...
"This thesis investigates several non-linear analogues of Lagrange functions in the hope of answerin...
In this thesis, new methods for large-scale non-linear optimization are presented. In particular, an...
Typical optimization problems aim to select a single solution of maximum or minimum value from a lar...
This document is the result of a reorganization of lecture notes used by the author during the Teach...
In this dissertation, a theoretical framework based on concentration inequalities for empirical proc...
where the function f or its gradient rf are not directly accessible except through Monte Carlo estim...
In this thesis numerical optimization methods for single- and multi-objective design optimization wi...
Paaßen B, Artelt A, Hammer B. Lecture Notes on Applied Optimization. Faculty of Technology, Bielefel...
This book is about learning for problem solving. [...] Human problem solving is strongly connected t...
This document provides a global view on my research on mathematical optimization. Over the last 10 y...
Model-based optimization methods are a class of random search methods that are useful for solving gl...
Continuous optimization is never easy: the exact solution is always a luxury demand and ...
Masteroppgave i informasjons- og kommunikasjonsteknologi IKT590 2013 – Universitetet i Agder, Grims...
Optimisation problems are of prime importance in scientific and engineering communities. Many day-t...
Machine learning has been a topic in academia and industry for decades. Performance of machine lear...
"This thesis investigates several non-linear analogues of Lagrange functions in the hope of answerin...
In this thesis, new methods for large-scale non-linear optimization are presented. In particular, an...
Typical optimization problems aim to select a single solution of maximum or minimum value from a lar...
This document is the result of a reorganization of lecture notes used by the author during the Teach...
In this dissertation, a theoretical framework based on concentration inequalities for empirical proc...
where the function f or its gradient rf are not directly accessible except through Monte Carlo estim...
In this thesis numerical optimization methods for single- and multi-objective design optimization wi...