Deterministic global optimization - theory, methods and applications

By Christodoulos A. Floudas. Nonconvex Optimization and Its Applications, 2010

409 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/0a40d5cbb5b380b65ad194f8e30a4c2efc2fa179

How researchers see this paper

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

In Computational Theory and Mathematics

1. John L. Klepeis
2. Tomasa Calvo
3. Gleb Beliakov
4. Ana Pradera
5. Christodoulos A. Floudas

In Physical and Theoretical Chemistry

1. John L. Klepeis
2. Tomasa Calvo
3. Gleb Beliakov
4. Ana Pradera

Citations in the corpus

• A review of recent advances in global optimization (2009, neutral): “Harding and Floudas (2000b) studied the enclosure of all heterogeneous and reactive azeotropes, developed a rigorous framework based on the αBB global optimization principles, and demonstrated its potential for a variety of case studies.”
• A Multi-Facet Survey on Memetic Computation (2011, neutral): “hand, comes in the form of both deterministic [70] and”
• The SCIP Optimization Suite 3.2 (2016, neutral): “Since an edge-concave function f : [`, u] → R admits a vertex polyhedral [83] convex envelope, it is possible to obtain the value of the convex envelope for x ∈ [`, u] by solving the following linear program [62, 30]:”
• Piecewise-Linear Approximations of Multidimensional Functions (2010, neutral): “[12, 3] optimized the profitability of a petroleum field with lift gas, integrating factors such as pressure dro p across tubing, line merging, multiphase flow, and reservoir pressure into their optimization model.”
• Global optimization issues in deep network regression: an overview (2018, neutral): “can be applied, both deterministic [59,79] and stochastic [76] ones.”
• Aggregation Functions: A Guide for Practitioners (2007, positive): “, systematic exploration of the domain, possible with the help of local optimization), Branch-andBound methods (especially the αBB method [95]), space-filling curves (i.”
• Global Optimization Algorithms -- Theory and Application (2009, neutral): “Chemistry, Chemical Engineering [175, 176, 177] Biochemistry [178] Constraint Satisfaction Problems (CSP) [6] Multi-Criteria Decision Making (MCDM) [19, 17] Biology [177]”
• A Polynomial Chaos-Based Kalman Filter Approach for Parameter Estimation of Mechanical Systems (2010, neutral): “It can be numerically solved by gradient methods [19] or by global optimization methods [20-24].”
• ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence. (2003, positive): “These new optimization methods take the form of hybrids between the deterministic global optimization algorithm, the aBB (Adjiman et al., 1998a,b; Floudas, 2000; Klepeis et al., 1998, 1999, 2002; Klepeis and Floudas, 1999), and a stochastically-based method, conformational space annealing (CSA)…”
• Review of Nonlinear Mixed-Integer and Disjunctive Programming Techniques (2002, neutral): “…combined with global optimization techniques for continuous variables (Falk and Soland, 1969; Horst and Tuy, 1996; Ryoo and Sahinidis, 1995; Quesada and Grossmann, 1995; Grossmann, 1996; Zamora and Grossmann, 1999; Floudas, 2000), which usually take the form of spatial branch and bound methods.”