Implementation of Cartesian grids to accelerate Delaunay-based derivative-free optimization

Beyhaghi, Pooriya; Bewley, Thomas

doi:10.1007/s10898-017-0548-3

Cited by 7 publications

(15 citation statements)

References 13 publications

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“…A comparison between the minima of these two search functions is made in order to decide between further sampling (and, therefore, refining) an existing measurement, or sampling at a new point in parameter space. The method developed builds closely on the Delaunay-based Derivative-free Optimization via Global Surrogates algorithm, dubbed ∆-DOGS, proposed in [12][13][14]. Convergence of the algorithm is established in problems for which a.…”

Section: Discussionmentioning

confidence: 99%

“…Algorithm 1 presents a strawman form of the ∆-DOGS(Z) algorithm. A significant refinement of this algorithm is presented as Algorithm 2 of [12], together with its proof of convergence and its implementation on model problems.…”

Section: Delaunay-based Optimization Coordinated With a Grid: ∆-Dogs(z)mentioning

confidence: 99%

“…In this paper, the Delaunay-based derivative-free optimization algorithm developed in [1,2,[12][13][14]47], dubbed ∆-DOGS, is modified to minimize an objective function, f (x) : R n → R, which cannot be evaluated directly, but can be approximated to a tuneable degree of precision. As the precision of any function evaluation is increased, the computational (or experimental) cost of that function evaluation is increased accordingly.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a provably globally convergent (under the appropriate assumptions) new optimization approach is developed for problems of the form given in (1). The structure of the remainder of the paper is as follows: Section 2 briefly reviews the key features of the ∆-DOGS(Z) algorithm developed in [12], upon which the present paper is built. Section 3 lays out all of the new elements that compose the new optimization approach, as well as the new algorithm itself, dubbed α-DOGS.…”

Section: Introductionmentioning

confidence: 99%

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A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging

2020

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This paper considers the efficient minimization of the infinite time average of a stationary ergodic process in the space of a handful of design parameters which affect it. Problems of this class, derived from physical or numerical experiments which are sometimes expensive to perform, are ubiquitous in engineering applications. In such problems, any given function evaluation, determined with finite sampling, is associated with a quantifiable amount of uncertainty, which may be reduced via additional sampling. The present paper proposes a new optimization algorithm to adjust the amount of sampling associated with each function evaluation, making function evaluations more accurate (and, thus, more expensive), as required, as convergence is approached. The work builds on our algorithm for Delaunay-based Derivative-free Optimization via Global Surrogates (∆-DOGS, see JOGO

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Section: Discussionmentioning

confidence: 99%

Section: Delaunay-based Optimization Coordinated With a Grid: ∆-Dogs(z)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging

2020

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“…One of the main challenges for Hough transform is tuning hyperparameters (e.g., distance resolution, angle resolution, an accumulator threshold parameter, minimum line length, and maximum line gap) in an efficient and accurate way for each image. This fine tuning can be easily be automated using blackbox optimization schemes such as [23,24]. One of the most common variants of the HLT is the Probabilistic HLT [20], in which a random, smaller subset of the original significant points is used for computation instead.…”

Section: Hough Line Transformmentioning

confidence: 99%

Robust Features Extraction for On-board Monocular-based Spacecraft Pose Acquisition

Capuano

Alimo

et al. 2019

AIAA Scitech 2019 Forum

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This paper presents the design, implementation, and validation of a robust feature extraction architecture for real-time on-board monocular vision-based pose initialization of a target spacecraft in application to on-orbit servicing and formation flying. The proposed computer vision algorithm is designed to detect the most significant features of an uncooperative target spacecraft in a sequence of two-dimensional input images that are collected on board the chaser spacecraft. A novel approach based on the fusion of multiple and parallel processing streams is proposed to filter a minimum number of extracted true point features, even in case of unfavourable illumination conditions and in presence of Earth in the background. These are then combined into relevant polyline structures that characterize the true geometrical shape of the target spacecraft.

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Design of IMEXRK time integration schemes via Delaunay-based derivative-free optimization with nonconvex constraints and grid-based acceleration

et al. 2020

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Implementation of Cartesian grids to accelerate Delaunay-based derivative-free optimization

Cited by 7 publications

References 13 publications

A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging

A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging

Robust Features Extraction for On-board Monocular-based Spacecraft Pose Acquisition

Design of IMEXRK time integration schemes via Delaunay-based derivative-free optimization with nonconvex constraints and grid-based acceleration

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