The Experts below are selected from a list of 3582 Experts worldwide ranked by ideXlab platform

Ebru Angün - One of the best experts on this subject based on the ideXlab platform.

  • Response surface methodology's Steepest Ascent and step size revisited: Correction
    European Journal of Operational Research, 2006
    Co-Authors: Jack P. C. Kleijnen, Dick Den Hertog, Ebru Angün
    Abstract:

    Abstract This Short Communication corrects Table 1 and Fig. 2 , Fig. 3 that were published in a recent article by the same authors, in this journal. The article discussed response surface methodology (RSM), which searches for the input combination maximizing the output of a real or simulated system. RSM uses Steepest Ascent (SA), which is scale-dependent. The article derived scale-independent ‘adapted’ SA (ASA). The two search directions were explored in Monte Carlo experiments. Unfortunately, the canonical and the non-canonical cases were mixed up. This Communication still shows that—in general—ASA gives a better search direction than SA.

  • Response Surface Methodology's Steepest Ascent and Step Size Revisited
    European Journal of Operational Research, 2004
    Co-Authors: Jack P. C. Kleijnen, Dick Den Hertog, Ebru Angün
    Abstract:

    Response Surface Methodology (RSM) searches for the input combination maximizing the output of a real system or its simulation.RSM is a heuristic that locally fits first-order polynomials, and estimates the corresponding Steepest Ascent (SA) paths.However, SA is scale-dependent; and its step size is selected intuitively.To tackle these two problems, this paper derives novel techniques combining mathematical statistics and mathematical programming.Technique 1 called 'adapted' SA (ASA) accounts for the covariances between the components of the estimated local gradient.ASA is scale-independent.The step-size problem is solved tentatively.Technique 2 does follow the SA direction, but with a step size inspired by ASA.Mathematical properties of the two techniques are derived and interpreted; numerical examples illustrate these properties.The search directions of the two techniques are explored in Monte Carlo experiments.These experiments show that - in general - ASA gives a better search direction than SA.

S D Morgera - One of the best experts on this subject based on the ideXlab platform.

  • matching schemes using the Steepest Ascent descent methods
    International Conference on Acoustics Speech and Signal Processing, 1991
    Co-Authors: P L C Cheong, S D Morgera
    Abstract:

    A tool is presented for solving some types of pattern matching problems using the method of Steepest-descent/Ascent. These matching problems can be recast as function maximization problems with the functions to be maximized being constrained over the orthogonal group. The optimization of these functions is then performed using the Steepest-Ascent algorithm. This is made possible by representing the orthogonal group as a Lie group and then investigating the gradient vector field associated with the function to be maximized or minimized. Since the orthogonal group includes the group of permutations as a subgroup, the proposed procedure works not only for the continuous optimization problem, but also for the combinatorial problem. The conditions for the convergence of the Steepest-Ascent algorithms are also shown and simulations are performed. >

  • ICASSP - Matching schemes using the Steepest-Ascent/descent methods
    [Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991
    Co-Authors: P L C Cheong, S D Morgera
    Abstract:

    A tool is presented for solving some types of pattern matching problems using the method of Steepest-descent/Ascent. These matching problems can be recast as function maximization problems with the functions to be maximized being constrained over the orthogonal group. The optimization of these functions is then performed using the Steepest-Ascent algorithm. This is made possible by representing the orthogonal group as a Lie group and then investigating the gradient vector field associated with the function to be maximized or minimized. Since the orthogonal group includes the group of permutations as a subgroup, the proposed procedure works not only for the continuous optimization problem, but also for the combinatorial problem. The conditions for the convergence of the Steepest-Ascent algorithms are also shown and simulations are performed. >

Pascalis Raimondos-møller - One of the best experts on this subject based on the ideXlab platform.

  • Steepest Ascent tariff reform
    Economic Theory, 2014
    Co-Authors: Pascalis Raimondos-møller, Alan D. Woodland
    Abstract:

    The policy reform literature is primarily concerned with the construction of reforms that yield welfare gains. By contrast, this paper’s contribution is to develop a theoretical concept for which the focus is upon the sizes of welfare gains accruing from policy reforms rather than upon their signs. In undertaking this task, and by focusing on tariff reforms, we introduce the concept of a Steepest Ascent policy reform, which is a locally optimal reform in the sense that it achieves the highest marginal gain in utility of any feasible local reform. We argue that this reform presents itself as a natural benchmark for the evaluation of the welfare effectiveness of other popular tariff reforms such as the proportional tariff reduction and the concertina rules, since it provides the maximal welfare gain of all possible local reforms. We derive properties of the Steepest Ascent tariff reform, construct an index to measure the relative welfare effectiveness of any given tariff reform, determine conditions under which proportional and concertina reforms are locally optimal and provide illustrative examples.

  • Steepest Ascent Tariff Reforms
    Economic Theory, 2013
    Co-Authors: Pascalis Raimondos-møller, Alan B. Woodland
    Abstract:

    This paper introduces the concept of a Steepest Ascent tariff reform for a small open economy. By construction, it is locally optimal in that it yields the highest gain in utility of any feasible tariff reform vector of the same length. Accordingly, it provides a convenient benchmark for the evaluation of the welfare effectiveness of other well known tariff reform rules, as e.g. the proportional and the concertina rules. We develop the properties of this tariff reform, characterize the sources of the potential welfare gains from tariff reform, use it to establish conditions under which some existing reforms are locally optimal, provide geometric illustrations and compare welfare effectiveness of reforms using numerical examples. Moreover, being a general concept, we apply it to the issue of market access and examine its implications. Overall, the paper’s contribution lies in presenting a theoretical concept where the focus is upon the size of welfare gains accruing from tariff reforms rather than simply with the direction of welfare effects that has been the concern of the literature.

Jack P. C. Kleijnen - One of the best experts on this subject based on the ideXlab platform.

  • Response surface methodology's Steepest Ascent and step size revisited: Correction
    European Journal of Operational Research, 2006
    Co-Authors: Jack P. C. Kleijnen, Dick Den Hertog, Ebru Angün
    Abstract:

    Abstract This Short Communication corrects Table 1 and Fig. 2 , Fig. 3 that were published in a recent article by the same authors, in this journal. The article discussed response surface methodology (RSM), which searches for the input combination maximizing the output of a real or simulated system. RSM uses Steepest Ascent (SA), which is scale-dependent. The article derived scale-independent ‘adapted’ SA (ASA). The two search directions were explored in Monte Carlo experiments. Unfortunately, the canonical and the non-canonical cases were mixed up. This Communication still shows that—in general—ASA gives a better search direction than SA.

  • Response Surface Methodology's Steepest Ascent and Step Size Revisited
    European Journal of Operational Research, 2004
    Co-Authors: Jack P. C. Kleijnen, Dick Den Hertog, Ebru Angün
    Abstract:

    Response Surface Methodology (RSM) searches for the input combination maximizing the output of a real system or its simulation.RSM is a heuristic that locally fits first-order polynomials, and estimates the corresponding Steepest Ascent (SA) paths.However, SA is scale-dependent; and its step size is selected intuitively.To tackle these two problems, this paper derives novel techniques combining mathematical statistics and mathematical programming.Technique 1 called 'adapted' SA (ASA) accounts for the covariances between the components of the estimated local gradient.ASA is scale-independent.The step-size problem is solved tentatively.Technique 2 does follow the SA direction, but with a step size inspired by ASA.Mathematical properties of the two techniques are derived and interpreted; numerical examples illustrate these properties.The search directions of the two techniques are explored in Monte Carlo experiments.These experiments show that - in general - ASA gives a better search direction than SA.

  • Stochastics and Statistics Response surface methodologys Steepest Ascent and step size revisited
    2004
    Co-Authors: Jack P. C. Kleijnen, Dick Den Hertog, Ebru Ang
    Abstract:

    Response surface methodology (RSM) searches for the input combination maximizing the output of a real system or its simulation. RSM is a heuristic that locally fits first-order polynomials, and estimates the corresponding Steepest Ascent (SA) paths. However, SA is scale-dependent; and its step size is selected intuitively. To tackle these two problems, this paper derives novel techniques combining mathematical statistics and mathematical programming. Technique 1, called adapted SA (ASA), accounts for the covariances between the components of the estimated local gradient. ASA is scale-independent. The step-size problem is solved tentatively. Technique 2 does follow the SA direction, but with a step size inspired by ASA. Mathematical properties of the two techniques are derived and interpreted; numerical examples illustrate these properties. The search directions of the two techniques are explored in Monte Carlo experiments. These experiments show that––in general––ASA gives a better search direction than SA. 2003 Elsevier B.V. All rights reserved.

P L C Cheong - One of the best experts on this subject based on the ideXlab platform.

  • matching schemes using the Steepest Ascent descent methods
    International Conference on Acoustics Speech and Signal Processing, 1991
    Co-Authors: P L C Cheong, S D Morgera
    Abstract:

    A tool is presented for solving some types of pattern matching problems using the method of Steepest-descent/Ascent. These matching problems can be recast as function maximization problems with the functions to be maximized being constrained over the orthogonal group. The optimization of these functions is then performed using the Steepest-Ascent algorithm. This is made possible by representing the orthogonal group as a Lie group and then investigating the gradient vector field associated with the function to be maximized or minimized. Since the orthogonal group includes the group of permutations as a subgroup, the proposed procedure works not only for the continuous optimization problem, but also for the combinatorial problem. The conditions for the convergence of the Steepest-Ascent algorithms are also shown and simulations are performed. >

  • ICASSP - Matching schemes using the Steepest-Ascent/descent methods
    [Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991
    Co-Authors: P L C Cheong, S D Morgera
    Abstract:

    A tool is presented for solving some types of pattern matching problems using the method of Steepest-descent/Ascent. These matching problems can be recast as function maximization problems with the functions to be maximized being constrained over the orthogonal group. The optimization of these functions is then performed using the Steepest-Ascent algorithm. This is made possible by representing the orthogonal group as a Lie group and then investigating the gradient vector field associated with the function to be maximized or minimized. Since the orthogonal group includes the group of permutations as a subgroup, the proposed procedure works not only for the continuous optimization problem, but also for the combinatorial problem. The conditions for the convergence of the Steepest-Ascent algorithms are also shown and simulations are performed. >