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Open AccessReportDOI

User guide for MINPACK-1. [In FORTRAN]

TLDR
A pack of FORTRAN subprograms for the numerical solution of nonlinear equations and nonlinear least-squares problems and this report provides an overview of the algorithms and software in the package.
Abstract
MINPACK-1 is a pack of FORTRAN subprograms for the numerical solution of nonlinear equations and nonlinear least-squares problems. This report provides an overview of the algorithms and software in the package, and includes the documentation and program listings.

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SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics.
Journal ArticleDOI

SciPy 1.0: fundamental algorithms for scientific computing in Python.

TL;DR: SciPy as discussed by the authors is an open-source scientific computing library for the Python programming language, which has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year.
Journal ArticleDOI

Parametric Recovery of Line‐of‐Sight Velocity Distributions from Absorption‐Line Spectra of Galaxies via Penalized Likelihood

TL;DR: In this paper, the authors investigate the accuracy of the parametric recovery of the line-of-sight velocity distribution (LOSVD) of the stars in a galaxy while working in pixel space.
Journal ArticleDOI

The Lennard-Jones equation of state revisited

TL;DR: In this paper, a modified Benedict-Webb-Rubin (MBWR) equation of state for the Lennard-Jones (LJ) fluid is presented and compared with the simulation data of this work, and previously published Gibbs ensemble data.
Journal ArticleDOI

Estimation of planar curves, surfaces, and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation

TL;DR: It is shown how this unified representation can be used for object recognition, object position estimation, and segmentation of objects into meaningful subobjects, that is, the detection of 'interest regions' that are more complex than high curvature regions and, hence, more useful as features for object Recognition.