pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization

Perez, Ruben E.; Jansen, Peter; Martins, Joaquim R. R. A.

doi:10.1007/s00158-011-0666-3

Cited by 386 publications

(196 citation statements)

References 26 publications

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Section: Parameter Estimation and Model Selection With ›A›imentioning

confidence: 99%

“…First we performed a coarse optimization using the augmented Lagrangian particle swarm optimizer (Jansen and Perez 2011), and then refined this solution using sequential least squares quadratic programming (Kraft 1988). Both of these techniques are implemented in the pyOpt package (version 1.2.0) for optimization in Python (Perez et al 2012).To asses the accuracy of point estimation of parameters in the face of varying amounts of and genetic distances to selective sweeps, we optimized the parameters of each demographic model against each data set simulated under that model, comparing estimated values to the true values. As shown in Results, this approach was quite successful in recovering the true parameter values of each demographic model when applied to data simulated under neutrality.…”

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Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

2016

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The availability of large-scale population genomic sequence data has resulted in an explosion in efforts to infer the demographic histories of natural populations across a broad range of organisms. As demographic events alter coalescent genealogies, they leave detectable signatures in patterns of genetic variation within and between populations. Accordingly, a variety of approaches have been designed to leverage population genetic data to uncover the footprints of demographic change in the genome. The vast majority of these methods make the simplifying assumption that the measures of genetic variation used as their input are unaffected by natural selection. However, natural selection can dramatically skew patterns of variation not only at selected sites, but at linked, neutral loci as well. Here we assess the impact of recent positive selection on demographic inference by characterizing the performance of three popular methods through extensive simulation of data sets with varying numbers of linked selective sweeps. In particular, we examined three different demographic models relevant to a number of species, finding that positive selection can bias parameter estimates of each of these models-often severely. We find that selection can lead to incorrect inferences of population size changes when none have occurred. Moreover, we show that linked selection can lead to incorrect demographic model selection, when multiple demographic scenarios are compared. We argue that natural populations may experience the amount of recent positive selection required to skew inferences. These results suggest that demographic studies conducted in many species to date may have exaggerated the extent and frequency of population size changes.KEYWORDS population genetics; positive selection; demographic inference T HE widespread availability of population genomic data has spurred a new generation of studies aimed at understanding the histories of natural populations from a host of model and nonmodel organisms alike. In particular, genomescale variation data allows for inference of demographic factors such as population size changes, the timing and ordering of population splits, migration rates between populations, and the founding of admixed populations (Pool et al. 2010;Pickrell and Pritchard 2012;Sousa and Hey 2013). Such efforts can refine our picture of demographic events inferred from the archaeological record (e.g., Fagundes et al. 2007;Goebel et al. 2008), or reveal such events in species where no archaeological data are available, and can aid conservation efforts by complementing census data (e.g., Hájková et al. 2007;Garrick et al. 2015).Population genomic data sets are well suited for this task simply because demographic changes leave their mark on patterns of genetic variation. Recent population growth, for example, will result in an excess of rare variation compared to equilibrium expectations (Fu 1997), while population contraction will result in an excess of intermediate frequency alleles (Maruyama and Fuerst 198...

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Section: Parameter Estimation and Model Selection With ›A›imentioning

confidence: 99%

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confidence: 99%

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

2016

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“…On the side, the whole optimization is set up and solved using the interface EOS (Environment for optimization and simulation) to the optimization package pyOpt (Perez et al 2012). EOS is written by da Rocha und the first author at the Institute of Lightweight Structures.…”

Section: Deducing the Optimization Modelmentioning

confidence: 99%

“…Acknowledgments The authors would like to thank all that contributed to the python-based optimization package pyOpt (Perez et al 2012).…”

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Multi-criteria optimization of an aircraft propeller considering manufacturing

Schatz¹,

Hermanutz²,

Baier³

2016

Struct Multidisc Optim

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Carbon-fiber-reinforced polymers are gaining ground; high mass-specific stiffness and strength properties in fiber direction are commonly identified as reasons. Nonetheless, there are great challenges unleashing the entire light-weight potential. For instance, the multitude of parameters (e.g. fiber orientations), also being linked with each other and having huge influence on, not only structural mechanics, but also onto effort in terms of manufacturing. Moreover, these parameters ideally need to be determined, such that mass and costs are minimal, while all structural and technical requirements are fulfilled. The challenge of considering manufacturing aspects along with structural mechanics are mainly addressed in this paper. It is outlining an approach for modeling manufacturing effort via expert knowledge and how to actually consider this model in a multi-criteria optimization framework. In addition, it will be shown how to incorporate these knowledge-based models into an efficient structural design optimization. For this sake, a braided propeller structure is optimized.

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“…A validation of the algorithms that were used in this study is presented in Supplementary material S1. A non-sorting genetic algorithm (NSGA-II) (Deb, 2001), as implemented in pyOpt (http://www.pyopt.org) (Perez et al, 2012) was used to extract the optimal Pareto Front after each optimisation phase.…”

Section: Surrogate Modelling Nsga-ii and Infill Strategymentioning

confidence: 99%

Multi-objective optimisation of stent dilation strategy in a patient-specific coronary artery via computational and surrogate modelling

Ragkousis

Curzen

Bressloff

2016

Journal of Biomechanics

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Multi-objective optimisation of stent dilation strategy in a patient-specific coronary artery via computational and surrogate modeling This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. vessel trauma is the damage caused by balloon dilation during stent deployment. In the present work, a Kriging based response surface modelling approach has been implemented to search for optimum stent deployment strategies in a clinically challenging, patient specific diseased coronary artery. In particular, the aims of this study were: i) to understand the impact of the balloon pressure and unpressurised diameter on stent malapposition, drug distribution and wall stresses via computer simulations and ii) obtain potentially optimal dilation protocols to simultaneously minimise stent malapposition and tissue wall stresses and maximise drug diffusion in the tissue. The results indicate that SM is inversely proportional to tissue stresses and drug deliverability. After analytical multi-objective optimisation, a set of "non-dominated" dilation scenarios was proposed as a post-optimisation methodology for protocol selection. Using this method, it has been shown that, for a given patient specific model, optimal stent expansion can be predicted. Such a framework could potentially be used by interventional cardiologists to minimise stent malapposition and tissue stresses whilst maximising drug deliverability in any patient-specific case.3

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pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization

Cited by 386 publications

References 26 publications

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

Multi-criteria optimization of an aircraft propeller considering manufacturing

Multi-objective optimisation of stent dilation strategy in a patient-specific coronary artery via computational and surrogate modelling

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