Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination

Gray, Genetha Anne.; Kolda, Tamara G.; Sale, Ken; Young, Malin M.

doi:10.1287/ijoc.1040.0102

Cited by 23 publications

(13 citation statements)

References 57 publications

(54 reference statements)

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“…Finally, we note that some work has also been done in comparing APPSPACK and other derivative-free optimization methods. Gray et al [2003] analyze the performance of APPSPACK and show that it is preferable to simulated annealing for a transmembrane protein structure prediction problem. Liang and Chen [2003] use NEOS [Dolan et al 2002] to compare APPSPACK to a limited-memory quasi-Newton method for optimal control of a fed-batch fermentation process and concluded that the APPS algorithm is a more powerful tool for stochastic optimization problems.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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].…”

mentioning

confidence: 99%

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

Gray¹,

Kolda²

2004

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“…If no reduction to the OF is identified, the step length is reduced and the search is repeated. Under certain conditions, APPSPACK is guaranteed to converge to a local minimum, and for some global optimization problems it has demonstrated to perform competitively with global optimization methods while requiring fewer function evaluations [24].…”

Section: Numerical Optimizationmentioning

confidence: 99%

Design Optimization of Vena Cava Filters: An Application to Dual Filtration Devices

Singer

Wang

Diachin³

2010

Journal of Biomechanical Engineering

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Background: Pulmonary embolism (PE) is a significant medical problem that results in over 300,000 fatalities per year. A common preventative treatment for PE is the insertion of a metallic filter into the inferior vena cava that traps thrombi before they reach the lungs. The goal of this work is to use methods of mathematical modeling and design optimization to determine the configuration of trapped thrombi that minimizes the hemodynamic disruption. The resulting configuration has implications for constructing an optimally designed vena cava filter.Approach: Computational fluid dynamics is coupled with a nonlinear optimization algorithm to determine the optimal configuration of trapped model thrombus in the inferior vena cava. The location and shape of the thrombus are parameterized, and an objective function, based on wall shear stresses, determines the worthiness of a given configuration. The methods are fully automated and demonstrate the capabilities of a design optimization framework that is broadly applicable.Results: Changes to thrombus location and shape alter the velocity contours and wall shear stress profiles significantly. For vena cava filters that trap two thrombi simultaneously, the undesirable flow dynamics past one thrombus can be mitigated by leveraging the flow past the other thrombus. Streamlining the shape of thrombus trapped along the cava wall reduces the disruption to the flow, but increases the area exposed to abnormal wall shear stress. Conclusions:Computer-based design optimization is a useful tool for developing vena cava filters. Characterizing and parameterizing the design requirements and constraints is essential for constructing devices that address clinical complications. In addition, formulating a well-defined objective function that quantifies clinical risks and benefits is needed for designing devices that are clinically viable.

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“…Derivative-free optimization is an area of long history and current rapid growth, fueled by a growing number of applications that range from science problems [4,42,52,143] to medical problems [90,103] to engineering design and facility location problems [2,10,15,48,49,54,57,91,92,98].…”

Section: Introductionmentioning

confidence: 99%

Derivative-free optimization: a review of algorithms and comparison of software implementations

2012

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This paper addresses the solution of bound-constrained optimization problems using algorithms that require only the availability of objective function values but no derivative information. We refer to these algorithms as derivative-free algorithms. Fueled by a growing number of applications in science and engineering, the development of derivativefree optimization algorithms has long been studied, and it has found renewed interest in recent time. Along with many derivative-free algorithms, many software implementations have also appeared. The paper presents a review of derivative-free algorithms, followed by a systematic comparison of 22 related implementations using a test set of 502 problems. The test bed includes convex and nonconvex problems, smooth as well as nonsmooth problems. The algorithms were tested under the same conditions and ranked under several criteria, including their ability to find near-global solutions for nonconvex problems, improve a given starting point, and refine a near-optimal solution. A total of 112,448 problem instances were solved. We find that the ability of all these solvers to obtain good solutions diminishes with increasing problem size. For the problems used in this study, TOMLAB/MULTI-MIN, TOMLAB/GLCCLUSTER, MCS and TOMLAB/LGO are better, on average, than other derivative-free solvers in terms of solution quality within 2,500 function evaluations. These global solvers outperform local solvers even for convex problems. Finally, TOMLAB/OQNLP, NEWUOA, and TOMLAB/MULTIMIN show superior performance in terms of refining a nearoptimal solution.

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Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination

Cited by 23 publications

References 57 publications

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

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

Design Optimization of Vena Cava Filters: An Application to Dual Filtration Devices

Derivative-free optimization: a review of algorithms and comparison of software implementations

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