Introduction to Global Optimization

By Leo Liberti. 2006

542 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a2b57036cd4c1385eb3ce7adb5b95ddcfd8016c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Representations of positive polynomials on noncompact semialgebraic sets via KKT ideals (2007, unknown): “18, [13]).”
The DC (Difference of Convex Functions) Programming and DCA Revisited with DC Models of Real World Nonconvex Optimization Problems (2005, background): “…solutions to nonconvex programs) whose main tools and solution methods are developed according to the spirit of the combinatorial optimization, but with the difference that one works in the continuous framework (see Horst and Tuy, 1996; Horst, Pardalos, and Thoai, 1995; Horst and Thoai, 1999).”
Variations and extension of the convex–concave procedure (2016, background): “Good overviews of the work that has been done in solving DC programs globally can be found in (Horst and Thoai, 1999; Horst et al, 1995), and the references therein.”
Reasoning about Hypothetical Agent Behaviours and their Parameters (2017, background): “Specifically, the estimation of parameter values can be viewed as a global optimisation problem [22] in which the goal is to find a parameter setting p with maximum probability over the history of observations H i .”
Disciplined convex-concave programming (2016, background): “Good overviews of solving DC programs globally can be found in [19], [20] and the references therein.”
Autonomous agents modelling other agents: A comprehensive survey and open problems (2017, methodology): “After new actions are observed, a subset of the types is selected and their parameter estimates updated using methods such as approximate Bayesian updating and exact global optimisation [145].”
Complete search in continuous global optimization and constraint satisfaction (2004, background): “…branch and bound in global optimization include Beale (1979, 1988), Berner (1996), Epperly and Pistikopoulos (1997), Epperly and Swaney (1996), Floudas (1995), Grossmann (1990), Horst et al. (1995), Jansson and Knüppel (1992), Kearfott (1996b), Pintér (1996a) and Van Hentenryck et al. (1997b).”
Global Optimization Algorithms -- Theory and Application (2009, background): “In this book, we will only be able to discuss a small fraction of the wide variety of global optimization techniques [7].”
Aggregation Functions: A Guide for Practitioners (2007, methodology): “Even if the class of the objective functions is limited to smooth or Lipschitz functions, the global optimization problem is NP-hard [128, 129, 130, 204].”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, background): “Optimization is an applied science which explores the best values of the parameters of a problem that may take under specified conditions (Corne et al. 1999; Horst et al. 2000).”
Mixed-integer nonlinear optimization*† (2013, background): “Huge literature on global optimization exists, including several textbooks (Hansen, 1992; Horst and Tuy, 1993; Horst et al., 1995; Floudas, 2000).”
Branch and bound with simplicial partitions for global optimization (2008, background): “Lipschitz optimization is based on assumption that the slope of an objective function is bounded [10].”
Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework (2013, background): “For a 136 non-convex optimization problem, most algorithms only guarantee the stationarity of a limit 137 point [46,71].”
Integrated supply chain design models: a survey and future research directions (2007, methodology): “Qi and Shen [60] propose an algorithm based on the bisection search and the outer approximation algorithm [39, 40].”