Numerical recipes in C++: The art of scientific computing

By F. Crimins. 2003

685 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/e84c5240050febbfc3af41b8e1ee991e14ba7aa9

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Citations in the corpus

SPACE TELESCOPE AND OPTICAL REVERBERATION MAPPING PROJECT. III. OPTICAL CONTINUUM EMISSION AND BROADBAND TIME DELAYS IN NGC 5548 (2015, methodology): “An analogy exists here with optimal (Weiner) filters—quoting from Numerical Recipes (Press et al. 2002, chap.”
A Poissonian explanation for heavy tails in e-mail communication (2008, methodology): “testing [29, 30] to assess the significance of the agreement b etween the predictions of our model and the empirical data (see SI Sec.”
Incremental Online Learning in High Dimensions (2005, background): “…P = ( X T X ) † , P z = ( Z T Z ) − 1 corresponds to the (pseudo)inverse covariance matrices and the symbol † represents the SVD pseudoinverse (Press, Flannery, Teukolsky, & Vetterling, 1989) that generates a solution to the inverse in the embedded lower-dimensional manifold of x with minimum…”
Dimension Reduction: A Guided Tour (2010, methodology): “2), which gives a much better approximation using simple interpolation [71].”
Chopped random-basis quantum optimization (2011, methodology): “The optimization problem is then reformulated as the extremization of the multivariable cost function F ( T,~c j ), which can be numerically approached with a suitable method, e.g., steepest descent, conjugate gradient or direct search methods [28].”
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them (2013, methodology): “Because the Matlab built-in function interp2 is based on cubic convolution (Keys 1981) and does not provide the derivatives used in interpolation, we use the spline-based implementation by Press et al. (2002).”
Deformable Medical Image Registration: A Survey (2013, methodology): “An optimization similar in spirit approach that has been used in an important number of registration problems is Powell's, or the Direction Set method [374].”
The 2dF-SDSS LRG and QSO Survey: The z<2.1 Quasar Luminosity Function from 5645 Quasars to g=21.85 (2005, methodology): “Subsequent pioneering work was carri ed out by many groups including Schmidt & Green (1983), Koo & Kron (1988), Hewett, Foltz & Chaffee (1993) and especially B oyle and collaborators (e.g. Boyle, Shanks & Peterson 1988), wit h extensions to highz (z > 3) being provided by Warren, Hewett & Osmer (199”
The method of fundamental solutions for eigenproblems in domains with and without interior holes (2006, methodology): “In the present work, we adopted the direct determinant search method to find the associated eigenvalues (Teukolsky et al. 1992; Karageorghis 2001).”
Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions (2002, methodology): “As an optimization algorithm, we have used a combination of gradient ascent and simulated annealing algorithms: successive line maximizations were done along the direction of the gradient (Press et al., 1992).”
Evaluating top-k queries over Web-accessible databases (2002, methodology): “Gaussian data set: We generate the multiattribute score distribution by producing ve overlapping multidimensional Gaussian bells [20].”
An Ammonia Spectral Atlas of Dense Cores in Perseus (2007, background): “(1994); Bachiller & Cernicharo (1986); Bachiller et al. (1987) and Juan et al. (1993). We find good agreement between our line widths and temperatures with . 20% variations across most of our sources. Discrepant points are invariably found in NGC 1333 where larger line widths are typically found in ”
A breast cancer prediction model incorporating familial and personal risk factors (2004, methodology): “This was done using a Newton–Raphson method [17].”
SNP Calling, Genotype Calling, and Sample Allele Frequency Estimation from New-Generation Sequencing Data (2012, methodology): “After tabulation of values of h ( v ) j we optimize the likelihood function for P using the BFGS algorithm [27].”
Finding Generators for Markov Chains via Empirical Transition Matrices, with Applications to Credit Ratings (2001, methodology): “As they have succinctly summarized, to determine if an empirical transition matrix is compatible with a true generator or an underlying Markov process is an embeddability problem; to seek for the true generator once its existence is established is an identification problem.”