Global Optimization Toolbox For Maple

Optimization is the science of finding solutions that satisfy complicated constraints and objectives. In engineering, constraints may arise from technical issues. In business, constraints are related to many factors, including cost, time, and staff. The objective of global optimization is to find [numerically] the absolute best solution of highly nonlinear optimization models that may have a number of locally optimal solutions. Global optimization problems can be extremely difficult. Frequently engineers and researchers are forced to settle for solutions that are “good enough” at the expense of extra time, money, and resources, because the best solution has not been found. Using the Global Optimization Toolbox, you can formulate your optimization model easily inside the powerful Maple numeric and symbolic system, and then use world-class Maple numeric solvers to return the best answer, fast!

This software is also referenced in ORMS.


References in zbMATH (referenced in 124 articles , 1 standard article )

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  1. Balsa-Canto, Eva; Alonso, Antonio A.; Arias-Méndez, Ana; García, Miriam R.; López-Núñez, A.; Mosquera-Fernández, Maruxa; Vázquez, C.; Vilas, Carlos: Modeling and optimization techniques with applications in food processes, bio-processes and bio-systems (2016)
  2. Gergel, Victor; Grishagin, Vladimir; Gergel, Alexander: Adaptive nested optimization scheme for multidimensional global search (2016)
  3. Paulavičius, Remigijus; Žilinskas, Julius: Advantages of simplicial partitioning for Lipschitz optimization problems with linear constraints (2016)
  4. Regis, Rommel G.: On the convergence of adaptive stochastic search methods for constrained and multi-objective black-box optimization (2016)
  5. Censor, Yair; Reem, Daniel: Zero-convex functions, perturbation resilience, and subgradient projections for feasibility-seeking methods (2015)
  6. Di Pillo, Gianni; Lucidi, Stefano; Rinaldi, Francesco: A derivative-free algorithm for constrained global optimization based on exact penalty functions (2015)
  7. Lampariello, F.; Liuzzi, G.: A filling function method for unconstrained global optimization (2015)
  8. Lampariello, Francesco; Liuzzi, Giampaolo: Global optimization of protein-peptide docking by a filling function method (2015)
  9. Liu, Haitao; Xu, Shengli; Ma, Ying; Wang, Xiaofang: Global optimization of expensive black box functions using potential Lipschitz constants and response surfaces (2015)
  10. Žilinskas, Antanas; Žilinskas, Julius: Adaptation of a one-step worst-case optimal univariate algorithm of bi-objective Lipschitz optimization to multidimensional problems (2015)
  11. Calvin, James M.; Žilinskas, Antanas: On a global optimization algorithm for bivariate smooth functions (2014)
  12. Wu, Zhi-you; Bai, Fu-sheng; Yang, Yong-jian; Jiang, Feng: Optimization methods for mixed integer weakly concave programming problems (2014)
  13. Žilinskas, Antanas: A one-step worst-case optimal algorithm for bi-objective univariate optimization (2014)
  14. Evtushenko, Yury; Posypkin, Mikhail: A deterministic approach to global box-constrained optimization (2013)
  15. Grbić, Ratko; Nyarko, Emmanuel Karlo; Scitovski, Rudolf: A modification of the DIRECT method for Lipschitz global optimization for a symmetric function (2013)
  16. Luo, Hezhi; Wu, Huixian; Liu, Jianzhen: Some results on augmented Lagrangians in constrained global optimization via image space analysis (2013)
  17. Pintér, János D.; Horváth, Zoltán: Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints (2013)
  18. Pintér, János D.; Kampas, Frank J.: Benchmarking nonlinear optimization software in technical computing environments (2013)
  19. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)
  20. Zhou, Enlu; Chen, Xi: Sequential Monte Carlo simulated annealing (2013)

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