Horst, R. and P. Pardalos (eds.): 1995, Handbook of Global Optimization. Dordrecht: Kluwer Academic Publishers. Home/Search Document Not in Database Summary Related Articles Check |
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Generalized Subinterval Selection Criteria for Interval Global.. - Csendes (Correct)
....paper extends the results of an earlier one, 5] for the more general case, when the global minimum value is not previously known. This work was supported by the Grants OMFB D 30 2000, OMFB E 24 2001, OTKA T 032118 and T 034350. 1 Consider the bound constrained global optimization problem [10, 19] min x2X f(x) 1) where the n dimensional interval X is the search region, and f(x) R R is the objective function. We assume that there exists at least one global minimizer point in X, that is also a stationary point. Problems that have only global minimizer points on the boundary of the ....
R. Horst and P.M. Pardalos (eds.), Handbook of Global Optimization, Kluwer, Dordrecht, 1995.
Lower Bound Functions for Polynomials - Garloff, Jansson, Smith (2003) (1 citation) (Correct)
....constructed to solve nonlinear systems and global optimisation problems. Relaxation techniques were rst discussed in the case of linear integer problems, and later also for special structured continuous global optimisation problems; see for example the monographs of Floudas [9] Horst and Pardalos [13], and Parker and Rardin [22] Linear relaxations for bilinear problems were rst considered by Al Khayyal and Falk [2] They use the convex envelope of bilinear terms in order to obtain a relaxation. For developments and improvements for special structured continuous global optimisation problems ....
R. Horst and P.M. Pardalos. Handbook of Global Optimization. Kluwer Academic Publishers, Dordrecht, Boston, London, 1995.
New Interval Methods for Constrained Global Optimization - Markót, Fernández.. (Correct)
....and Electronics, University of Almer a, Spain, email: leo ace.ual.es Mathematics Subject Classi cation (1991) 65G10, 90C30, 65K05, 90B85 2 M.Cs. Mark ot et al. function f and or on the constraint functions g j . For an introduction to these methods, the interested reader is referred to [24,25]. One of the most successful methods dealing with (1) use interval analysis in branch and bound algorithms. Interval branch and bound methods work in a reliable way (usually they apply interval arithmetic [23,28,36] to have inclusion functions for the objective function and for the constraint ....
Horst R. and Pardalos P.M (eds.) (1995), Handbook of Global Optimization, Kluwer, Dordrecht.
Evolutionary Diffusion Optimization, Part II: Performance.. - Tsui, Liu (2002) (Correct)
....the space and computational time required of the search algorithm. It also makes the exhaustive search, one which tries out all possible combinations of values of all parameters, unfeasible. Many algorithms have been developed over the years to tackle the challenging task of global optimization [2]. Some well known stochastic search algorithm such as simulated annealing [3] evolutionary algorithms [4] particle swarm [5] cultural algorithm [6] and ant colony optimization [7] have been applied to optimization tasks. With the exception of the SA, all the above search algorithms are ....
Reiner Horst and Panos M. Pardalos, Eds., Handbook of Global Optimization, Kluwer Academic Publishers, 1995.
Evolutionary Diffusion Optimization, Part I: Description of the.. - Tsui, Liu (2002) (Correct)
....as the minimization of function F . Turning the sign in equation 1 around makes the search for x a maximization task. They can collectively be called global optimization tasks [1] Many algorithms have been developed over the years to tackle the challenging task of global optimization [2] [3], 4] There are several aspects to the challenge any new search algorithm will face. Firstly, the landscape of the function to be optimized is unknown. Unimodal functions can be monotonic in nature and the search is easy once the downhill direction is found. Multimodal functions, on the other ....
Reiner Horst and Panos M. Pardalos, Eds., Handbook of Global Optimization, Kluwer Academic Publishers, 1995.
Global Optimization For Constrained Nonlinear Programming - Wang (2001) (5 citations) (Correct)
....interval methods require the continuity and di#erentiability of functions and cannot be used for solving discrete and mixed integer NLPs. Pure random search samples uniformly a search space, while adaptive random search guides sampling based on information derived from its previous samples [104]. Both methods are stochastic approaches that guarantee the reachability of CGM cn because any point in a search space has a chance to be found. The sequence of sample points, however, may not 18 converge, or even diverge, and fail to provide asymptotic convergence. In addition, both methods may ....
....to overcome some infeasible regions by large increases in the values of its penalty functions, making it di#cult for SA to move from one feasible region to another or escape from local minima, especially at low temperatures. 2.1. 3 Lagrangian Formulations for Continuous NLPs Lagrangian methods [31, 125, 145, 69, 104] were traditionally developed to solve continuous NLPs with di#erentiability. They utilize Lagrange multipliers to combine constraints with the objective function to form a Lagrangian function and then gradually resolve the constraints through iterative updates in Lagrangian search space (the ....
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R. Horst and P. M. Pardalos. Handbook of Global Optimization. Kluwer Academic Publishers, 1995.
Optimal Anytime Search For Constrained Nonlinear Programming - Chen (2001) (4 citations) (Correct)
....infeasible points. This approach, however, has di#culty in handling nonlinear 16 constraints whose feasible regions may be very hard to locate, leading to the generation of mostly infeasible points that are rejected. The other approach is based on enumeration or randomized search techniques [95]. Enumerative branch and bound algorithms [108, 165] decompose a search space and estimate the bound for each in order to eliminate infeasible subspaces. In these algorithms, branching variables are chosen according to a fixed a priori ordering, and functions may be approximated by piecewise ....
R. Horst and P. M. Pardalos. Handbook of Global Optimization. Kluwer Academic Publishers, 1995.
Extended Frontiers in Optimization Techniques - Butenko, Pardalos Self-citation (Pardalos) (Correct)
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R. Horst and P.M. Pardalos. Handbook of Global Optimization. Kluwer Academic Publishers, 1995.
Global Optimization Approach to Unequal Sphere Packing Problems.. - Sutou, Dai (2002) Self-citation (Pardalos) (Correct)
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HORST, R., and PARDALOS,P.M.,Handbook of Global Optimization, Kluwer Academic Publishers, Dordrecht, Holland, 1995.
Global Optimization Using a Dynamical Systems Approach - Sertl, Dellnitz (2005) (Correct)
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Horst, R. and P. Pardalos (eds.): 1995, Handbook of Global Optimization. Dordrecht: Kluwer Academic Publishers.
Emotional Ant Based Modeling of Crowd Dynamics - Soumya Banarjee Department (Correct)
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Horst R, Pardalos, P.M. Handbook of Global Optimization, Kluwer Academic Publishers, 1995
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Horst R, Pardalos, P.M. editors, Handbook of Global Optimization, Kluwer Academic Publishers, 1995
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Horst R. and P.M. Pardalos (eds.) (1994), Handbook of Global Optimization, Kluwer Academic Publishers.
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Reiner Horst and Panos M. Pardalos. Handbook of Global Optimization. Kluwer Academic Publishers, Dordrecht Boston London, 1995.
Comparative Assessment of Algorithms and Software for .. - Khompatraporn.. (Correct)
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Horst, R., and Pardalos, P.M. (Editors) (1995) Handbook of Global Optimization, Kluwer Academic Publishers, Dordrecht / Boston / London.
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R. Horst, P.M. Pardalos, Eds., Handbook of Global Optimization, Kluwer Academic Publishers, Dordrecht, 1995.
On the investigation of Stochastic Global Optimization.. - Baritompa, Hendrix (2001) (Correct)
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R. Horst and P. M. Pardalos, eds. Handbook of Global Optimization, Dordrecht: Kluwer, 1995.
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R. Horst and P.M. Pardalos (eds.), Handbook of global optimization, Kluwer 1995.
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Horst, R. and P.M. Pardalos, Handbook of Global Optimization, Kluwer, Dordrecht, 1995.
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