A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation

Powell, M. J. D.

doi:10.1007/978-94-015-8330-5_4

Cited by 856 publications

(574 citation statements)

References 7 publications

Supporting

Mentioning

570

Contrasting

Unclassified

Order By: Relevance

“…In order to speed up the global maximization process by reducing the memory access, we execute the basinhopping over a local maximization using only the continuous parameters (z, τ d , σ i ). For this process, we use the COBYLA method (Powell 1994).…”

Section: Shell-model Fittingmentioning

confidence: 99%

Modeling 237 Lyman-αspectra of the MUSE-Wide survey

Grönke

2017

A&A

View full text Add to dashboard Cite

We compare 237 Lyman-α (Lyα) spectra of the "MUSE-Wide survey" ) to a suite of radiative transfer simulations consisting of a central luminous source within a concentric, moving shell of neutral gas, and dust. This six parameter shell-model has been used numerously in previous studies, however, on significantly smaller datasets. We find that the shell-model can reproduce the observed spectral shape very well -better than the also common 'Gaussian-minus-Gaussian' model which we also fitted to the dataset. Specifically, we find that ∼ 94% of the fits possess a goodness-of-fit value of p(χ 2 ) > 0.1. The large number of spectra allows us to robustly characterize the shell-model parameter range, and consequently, the spectral shapes typical for realistic spectra. We find that the vast majority of the Lyα spectral shapes require an outflow and only ∼ 5% are well-fitted through an inflowing shell. In addition, we find ∼ 46% of the spectra to be consistent with a neutral hydrogen column density < 10 17 cm −2 -suggestive of a non-negligible fraction of continuum leakers in the MUSE-Wide sample. Furthermore, we correlate the spectral against the Lyα halo properties against each other but do not find any strong correlation.

show abstract

Section: Shell-model Fittingmentioning

confidence: 99%

Modeling 237 Lyman-αspectra of the MUSE-Wide survey

Grönke

2017

A&A

View full text Add to dashboard Cite

show abstract

“…By contrast, increasing the resonance frequency of a resonator is very di cult. The optimisation was carried out using the constrained optimisation by linear approximation (COBYLA) algorithm [22]. A typical optimisation trajectory is shown in Fig.…”

Section: Optimisationmentioning

confidence: 99%

An optimised detector for in-situ high-resolution NMR in microfluidic devices

Finch

Yılmaz

Utz

2016

Journal of Magnetic Resonance

109

View full text Add to dashboard Cite

Integration of high-resolution nuclear magnetic resonance (NMR) spectroscopy with microfluidic lab-on-a-chip devices is challenging due to limited sensitivity and line broadening caused by magnetic susceptibility inhomogeneities. We present a novel double-stripline NMR probe head that accommodates planar microfluidic devices, and obtains the NMR spectrum from a rectangular sample chamber on the chip with a volume of 2 l. Finite element analysis is used to jointly optimise the detector and sample volume geometry for sensitivity and RF homogeneity. A prototype of the optimised design has been built, and its properties have been characterised experimentally. The performance in terms of sensitivity and RF homogeneity closely agrees with the numerical predictions. The system reaches a mass limit of detection of 1.57 nMol p s, comparing very favourably with other micro-NMR systems. The spectral resolution of this chipGprobe system is better than 1.75 Hz at a magnetic field of 7 T, with excellent line shape.

show abstract

“…At this moment, to improve the performance, we adopt a local derivative-free optimization technique, called Constrained Optimization by Linear Approximation (COBYLA) [60]. This optimization algorithm is based on linear approximation of the optimized objective function and all the constraints and transform the original problem to a linear program to solve.…”

Section: Results For Hand Configuration Querymentioning

confidence: 99%

Dexterous grasping under shape uncertainty

Hang

Kragić

et al. 2016

Robotics and Autonomous Systems

View full text Add to dashboard Cite

An important challenge in robotics is to achieve robust performance in object grasping and manipulation, dealing with noise and uncertainty. This paper presents an approach for addressing the performance of dexterous grasping under shape uncertainty. In our approach, the uncertainty in object shape is parameterized and incorporated as a constraint into grasp planning. The proposed approach is used to plan feasible hand configurations for realizing planned contacts using different robotic hands. A compliant finger closing scheme is devised by exploiting both the object shape uncertainty and tactile sensing at fingertips. Experimental evaluation demonstrates that our method improves the performance of dexterous grasping under shape uncertainty.

show abstract

A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation

Cited by 856 publications

References 7 publications

Modeling 237 Lyman-αspectra of the MUSE-Wide survey

Modeling 237 Lyman-αspectra of the MUSE-Wide survey

An optimised detector for in-situ high-resolution NMR in microfluidic devices

Dexterous grasping under shape uncertainty

Contact Info

Product

Resources

About