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  • 1
    Electronic Resource
    Electronic Resource
    Pure adaptive search in global optimization (1992)
    Springer
    Mathematical programming 53 (1992), S. 323-338 
    Details
    ISSN: 1436-4646
    Keywords: Random search ; Monte Carlo optimization ; global optimization ; complexity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Pure adaptive seach iteratively constructs a sequence of interior points uniformly distributed within the corresponding sequence of nested improving regions of the feasible space. That is, at any iteration, the next point in the sequence is uniformly distributed over the region of feasible space containing all points that are strictly superior in value to the previous points in the sequence. The complexity of this algorithm is measured by the expected number of iterations required to achieve a given accuracy of solution. We show that for global mathematical programs satisfying the Lipschitz condition, its complexity increases at mostlinearly in the dimension of the problem.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01585710
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  • 2
    Electronic Resource
    Electronic Resource
    Improving Hit-and-Run for global optimization (1993)
    Springer
    Journal of global optimization 3 (1993), S. 171-192 
    Details
    ISSN: 1573-2916
    Keywords: Random search ; Monte Carlo optimization ; algorithm complexity ; global optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Improving Hit-and-Run is a random search algorithm for global optimization that at each iteration generates a candidate point for improvement that is uniformly distributed along a randomly chosen direction within the feasible region. The candidate point is accepted as the next iterate if it offers an improvement over the current iterate. We show that for positive definite quadratic programs, the expected number of function evaluations needed to arbitrarily well approximate the optimal solution is at most O(n5/2) wheren is the dimension of the problem. Improving Hit-and-Run when applied to global optimization problems can therefore be expected to converge polynomially fast as it approaches the global optimum.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01096737
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  • 3
    Electronic Resource
    Electronic Resource
    Simulated annealing for constrained global optimization (1994)
    Springer
    Journal of global optimization 5 (1994), S. 101-126 
    Details
    ISSN: 1573-2916
    Keywords: Continuous simulated annealing ; adaptive cooling ; random search ; global optimization ; Monte Carlo optimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Hide-and-Seek is a powerful yet simple and easily implemented continuous simulated annealing algorithm for finding the maximum of a continuous function over an arbitrary closed, bounded and full-dimensional body. The function may be nondifferentiable and the feasible region may be nonconvex or even disconnected. The algorithm begins with any feasible interior point. In each iteration it generates a candidate successor point by generating a uniformly distributed point along a direction chosen at random from the current iteration point. In contrast to the discrete case, a single step of this algorithm may generateany point in the feasible region as a candidate point. The candidate point is then accepted as the next iteration point according to the Metropolis criterion parametrized by anadaptive cooling schedule. Again in contrast to discrete simulated annealing, the sequence of iteration points converges in probability to a global optimum regardless of how rapidly the temperatures converge to zero. Empirical comparisons with other algorithms suggest competitive performance by Hide-and-Seek.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01100688
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