Numerical recipes in C. The art of scientific computing

By Max Planitz, W. Press, B. Flannery, Saul A. Teukolsky, W. Vetterling. 1987

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields (2017, methodology): “We implemented the safe bounded Newton-Raphson method from Press et al. (1992, Section 9.4) on the derivative of the line search function.”
Lines and Points in Three Views and the Trifocal Tensor (1997, methodology): “Let the singular value decomposition ([11]) be X = UDV , where D is a diagonal matrix diag(α, β, 0, 0).”
Policy gradient reinforcement learning for fast quadrupedal locomotion (2004, unknown): “To test the first possibility, we compared the performance of our approach to that of other parameter optimization methods, such as those presented by Press [15].”
Learning Ball Acquisition on a Physical Robot (2004, methodology): “The other algorithm, known as the downhill simplex or “amoeba” algorithm, involves moving a simplex of points through policy space [14].”
STHoles: a multidimensional workload-aware histogram (2001, background): “Gauss: The Gauss synthetic distributions [23] consist of a predetermined number of overlapping multidimensional gaussian bells.”
Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm (2005, methodology): “To minimize the target function, the Levenberg– Marquardt nonlinear least-squares-ﬁtting algorithm, described in detail by Press et al [74], has been used.”