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.
This software is also referenced in ORMS.
Keywords for this software
References in zbMATH (referenced in 143 articles , 1 standard article )
Showing results 1 to 20 of 143.
- Barkalov, Konstantin; Strongin, Roman: Solving a set of global optimization problems by the parallel technique with uniform convergence (2018)
- Calvin, James; Gimbutienė, Gražina; Phillips, William O.; Žilinskas, Antanas: On convergence rate of a rectangular partition based global optimization algorithm (2018)
- Evtushenko, Yuri; Posypkin, Mikhail; Rybak, Larisa; Turkin, Andrei: Approximating a solution set of nonlinear inequalities (2018)
- Tervo, J.; Kokkonen, P.; Frank, M.; Herty, M.: On existence of solutions for Boltzmann continuous slowing down transport equation (2018)
- Ernestus, Maximilian; Friedrichs, Stephan; Hemmer, Michael; Kokemüller, Jan; Kröller, Alexander; Moeini, Mahdi; Schmidt, Christiane: Algorithms for art gallery illumination (2017)
- Tarłowski, Dawid: On the convergence rate issues of general Markov search for global minimum (2017)
- Xue, Dingyü: Fractional-order control systems. Fundamentals and numerical implementations (2017)
- Al-Dujaili, Abdullah; Suresh, S.; Sundararajan, N.: MSO: a framework for bound-constrained black-box global optimization algorithms (2016)
- 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)
- Evtushenko, Yu. G.; Lurie, S. A.; Posypkin, M. A.; Solyaev, Yu. O.: Application of optimization methods for finding equilibrium states of two-dimensional crystals (2016)
- Gergel, Victor; Grishagin, Vladimir; Gergel, Alexander: Adaptive nested optimization scheme for multidimensional global search (2016)
- Paulavičius, Remigijus; Žilinskas, Julius: Advantages of simplicial partitioning for Lipschitz optimization problems with linear constraints (2016)
- Regis, Rommel G.: On the convergence of adaptive stochastic search methods for constrained and multi-objective black-box optimization (2016)
- Sergeyev, Yaroslav D.; Mukhametzhanov, Marat S.; Kvasov, Dmitri E.; Lera, Daniela: Derivative-free local tuning and local improvement techniques embedded in the univariate global optimization (2016)
- Censor, Yair; Reem, Daniel: Zero-convex functions, perturbation resilience, and subgradient projections for feasibility-seeking methods (2015)
- Di Pillo, Gianni; Lucidi, Stefano; Rinaldi, Francesco: A derivative-free algorithm for constrained global optimization based on exact penalty functions (2015)
- Lampariello, F.; Liuzzi, G.: A filling function method for unconstrained global optimization (2015)
- Lampariello, Francesco; Liuzzi, Giampaolo: Global optimization of protein-peptide docking by a filling function method (2015)
- Liu, Haitao; Xu, Shengli; Ma, Ying; Wang, Xiaofang: Global optimization of expensive black box functions using potential Lipschitz constants and response surfaces (2015)
- Žilinskas, Antanas: Visualization of a statistical approximation of the Pareto front (2015)