Asynchronous Parallel Pattern Search for Nonlinear Optimization

Hough, Patricia Diane; Kolda, Tamara G.; Torczon, Virginia

doi:10.1137/s1064827599365823

Cited by 141 publications

(113 citation statements)

References 17 publications

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“…The upper and lower bounds are optional on an element-byelement basis; specifically, l is an n-vector with entries in R∪{−∞} and u is an n-vector with entries in R∪{+∞}. To find a solution of (1), APPSPACK implements asynchronous parallel pattern search (APPS) [Hough et al 2001;Kolda 2004], a method in the class of direct search methods [Wright 1996; Lewis et al 2000]. APPS is a variant on generating set search as described by .…”

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confidence: 99%

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APPSPACK 4.0 : asynchronous parallel pattern search for derivative-free optimization.

Gray¹,

Kolda²

2004

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APPSPACK is software for solving unconstrained and bound constrained optimization problems. It implements an asynchronous parallel pattern search method that has been specifically designed for problems characterized by expensive function evaluations. Using APPSPACK to solve optimization problems has several advantages: No derivative information is needed; the procedure for evaluating the objective function can be executed via a separate program or script; the code can be run in serial or parallel, regardless of whether or not the function evaluation itself is parallel; and the software is freely available. We describe the underlying algorithm, data structures, and features of APPSPACK version 4.0 as well as how to use and customize the software.3

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confidence: 99%

“…See, for example, Hough et al [2001], Mathew et al [2002], Chiesa et al [2004], Kupinksi et al [2003], Croue [2003], Gray et al [2003], and Fowler et al [2004].…”

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confidence: 99%

APPSPACK 4.0 : asynchronous parallel pattern search for derivative-free optimization.

Gray¹,

Kolda²

2004

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“…Asynchronous parallel pattern search (APPS) retains the positive features of PPS, but it does not assume that the amount of time required for an objective function evaluation is constant or that the processors are homogeneous. It does not have any required synchronizations and thus requires less total time to return results that are comparable to those acheived by PPS [23]. Furthermore, it has been shown that APPS is globally convergent under the standard assumptions for PPS [29].…”

Section: Asynchronous Parallel Pattern Searchmentioning

confidence: 94%

Optimizing an emperical scoring function for transmembrane protein structure determination.

Young¹,

Sale²,

Gray³

et al. 2003

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We examine the problem of transmembrane protein structure determination. Like many other questions that arise in biological research, this problem cannot be addressed by traditional laboratory experimentation alone. An approach that integrates experiment and computation is required. We investigate a procedure which states the transmembrane protein structure determination problem as a bound constrained optimization problem using a special empirical scoring function, called Bundler, as the objective function. In this paper, we describe the optimization problem and some of its mathematical properties. We compare and contrast results obtained using two different derivative free optimization algorithms.

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“…The MFO approach described in this paper incorporates a derivative-free optimization method called Asynchronous Parallel Pattern Search (APPS) [18,19]. The APPS algorithm is part of a class of direct search methods which were primarily developed to address problems in which the derivative of the objective function is unavailable and approximations are unreliable [37,25].…”

Section: Asynchronous Parallel Pattern Search (Apps)mentioning

confidence: 99%

“…The APPS algorithm is a modification of PPS that eliminates the synchronization requirements. It retains the positive features of PPS, but eliminates processor latency and requires less total time than PPS to return results [18]. Implementations of APPS have minimal requirements on the number of processors and do not assume that the amount of time required for an objective function evaluation is constant or that the processors are homogeneous.…”

Section: Asynchronous Parallel Pattern Search (Apps)mentioning

confidence: 99%

Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions.

Hough

Gray

Castro

et al. 2006

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Many engineering application problems use optimization algorithms in conjunction with numerical simulators to search for solutions. The formulation of relevant objective functions and constraints dictate possible optimization algorithms. Often, a gradient based approach is not possible since objective functions and constraints can be nonlinear, nonconvex, non-differentiable, or even discontinuous and the simulations involved can be computationally expensive. Moreover, computational efficiency and accuracy are desirable and also influence the choice of solution method.With the advent and increasing availability of massively parallel computers, computational speed has increased tremendously. Unfortunately, the numerical and model complexities of many problems still demand significant computational resources. Moreover, in optimization, these expenses can be a limiting factor since obtaining solutions often requires the completion of numerous computationally intensive simulations. Therefore, we propose a multifidelity optimization algorithm (MFO) designed to improve the computational efficiency of an optimization method for a wide range of applications.In developing the MFO algorithm, we take advantage of the interactions between multifidelity models to develop a dynamic and computational time saving optimization algorithm. First, a direct search method is applied to the high fidelity model over a reduced design space. In conjunction with this search, a specialized oracle is employed to map the design space of this high fidelity model to that of a computationally cheaper low fidelity model using space mapping techniques. Then, in the low fidelity space, an optimum is obtained using gradient or non-gradient based optimization, and it is mapped back to the high fidelity space.In this paper, we describe the theory and implementation details of our MFO algorithm. We also demonstrate our MFO method on some example problems and on two applications: earth penetrators and groundwater remediation. 4 AcknowledgmentsThanks to Paul Demmie and Donald Longcope for valuable discussions and help with earth penetrator models used within this document. Thanks to Katherine Fowler for providing the groundwater remediation problem and promoting our optimization method to the groundwater community. Thanks to Mike Eldred for valuable discussions and suggestions about space mapping.5

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Asynchronous Parallel Pattern Search for Nonlinear Optimization

Cited by 141 publications

References 17 publications

APPSPACK 4.0 : asynchronous parallel pattern search for derivative-free optimization.

APPSPACK 4.0 : asynchronous parallel pattern search for derivative-free optimization.

Optimizing an emperical scoring function for transmembrane protein structure determination.

Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions.

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