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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
    Pure adaptive search in monte carlo optimization (1989)
    Springer
    Mathematical programming 43 (1989), S. 317-328 
    Details
    ISSN: 1436-4646
    Keywords: Random search ; Monte Carlo optimization ; convex programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Pure adaptive search constructs a sequence of points uniformly distributed within a corresponding sequence of nested regions of the feasible space. At any stage, the next point in the sequence is chosen uniformly distributed over the region of feasible space containing all points that are equal or superior in value to the previous points in the sequence. We show that for convex programs the number of iterations required to achieve a given accuracy of solution increases at most linearly in the dimension of the problem. This compares to exponential growth in iterations required for pure random search.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01582296
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  • 3
    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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