Cited by 7,103Matrix Theory and AlgorithmsStatistical and numerical algorithmsAdvanced Optimization Algorithms Research

The DOI matches its Crossref record.

Solving Least Squares Problems

Lawson, Charles L., Hanson, Richard J.·1995·Society for Industrial and Applied Mathematics eBooks

DOI 10.1137/1.9781611971217

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1137/1.9781611971217

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Sorted by citations

Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Tensor Decompositions and Applications
Kolda, T. G., & Bader, B. W. (2009) · SIAM Review · doi:10.1137/07070111x · Cited by 10,624
Least angle regression
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357
Principal component analysis in linear systems: Controllability, observability, and model reduction
Moore, B. C. (1981) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1981.1102568 · Cited by 5,289
Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
Hansen, P. C. (1992) · SIAM Review · doi:10.1137/1034115 · Cited by 3,903
Size-Distribution Analysis of Macromolecules by Sedimentation Velocity Ultracentrifugation and Lamm Equation Modeling
Schuck, P. (2000) · Biophysical Journal · doi:10.1016/s0006-3495(00)76713-0 · Cited by 3,868
A Double-Difference Earthquake Location Algorithm: Method and Application to the Northern Hayward Fault, California
Waldhauser, F. (2000) · Bulletin of the Seismological Society of America · doi:10.1785/0120000006 · Cited by 3,568
The physical properties of star-forming galaxies in the low-redshift universe
Brinchmann, J., Charlot, S., White, S. D. M., Tremonti, C., Kauffmann, G., Heckman, T., & Brinkmann, J. (2003) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2004.07881.x · Cited by 2,513
Deep learning with coherent nanophotonic circuits
Shen, Y., Harris, N. C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., Sun, X., Zhao, S., Larochelle, H., Englund, D., et al. (2017) · Nature Photonics · doi:10.1038/nphoton.2017.93 · Cited by 3,340
The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems
Hansen, P. C., & O’Leary, D. P. (1993) · SIAM Journal on Scientific Computing · doi:10.1137/0914086 · Cited by 3,158
PRIMUS: a Windows PC-based system for small-angle scattering data analysis
Konarev, P. V., Волков, В. В., Sokolova, A. V., Koch, M. H. J., & Svergun, D. I. (2003) · Journal of Applied Crystallography · doi:10.1107/s0021889803012779 · Cited by 2,850
PARAFAC. Tutorial and applications
Bro, R. (1997) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(97)00032-4 · Cited by 2,824
Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding
Bevilacqua, M., Roumy, A., Guillemot, C. M., & Morel, M.-L. A. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.135 · Cited by 3,162
Chemical mass transfer in magmatic processes IV. A revised and internally consistent thermodynamic model for the interpolation and extrapolation of liquid-solid equilibria in magmatic systems at elevated temperatures and pressures
Ghiorso, M. S., & Sack, R. O. (1995) · Contributions to Mineralogy and Petrology · doi:10.1007/bf00307281 · Cited by 2,782
CONTIN: A general purpose constrained regularization program for inverting noisy linear algebraic and integral equations
Provencher, S. W. (1982) · Computer Physics Communications · doi:10.1016/0010-4655(82)90174-6 · Cited by 2,739
Inference of population splits and mixtures from genome-wide allele frequency data
Pickrell, J. K., & Pritchard, J. K. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002967 · Cited by 2,731
A constrained regularization method for inverting data represented by linear algebraic or integral equations
Provencher, S. W. (1982) · Computer Physics Communications · doi:10.1016/0010-4655(82)90173-4 · Cited by 2,704
Parametric Recovery of Line‐of‐Sight Velocity Distributions from Absorption‐Line Spectra of Galaxies via Penalized Likelihood
Cappellari, M., & Emsellem, É. (2004) · Publications of the Astronomical Society of the Pacific · doi:10.1086/381875 · Cited by 2,215
New developments in the ATSAS program package for small-angle scattering data analysis
Petoukhov, M. V., Franke, D., Shkumatov, A. V., Tria, G., Kikhney, A., Gajda, M. J., Gorba, C., Mertens, H. D. T., Konarev, P. V., & Svergun, D. I. (2012) · Journal of Applied Crystallography · doi:10.1107/s0021889812007662 · Cited by 1,907
Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery
Heinz, D. C., & Chein-I-Chang (2001) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.911111 · Cited by 1,860
Factorization Methods for Discrete Sequential Estimation
(1977) · Mathematics in Science and Engineering/Mathematics in science and engineering · doi:10.1016/s0076-5392(08)x6052-7 · Cited by 1,851
BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data
Gascuel, O. (1997) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a025808 · Cited by 1,850
Projected Gradient Methods for Nonnegative Matrix Factorization
Lin, C. (2007) · Neural Computation · doi:10.1162/neco.2007.19.10.2756 · Cited by 1,768
REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems
Hansen, P. C. (1994) · Numerical Algorithms · doi:10.1007/bf02149761 · Cited by 1,725
EEG source imaging.
Michel, C., Murray, M. M., Lantz, G., Gonzalez, S., Spinelli, L., & de Peralta, R. G. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.06.001 · Cited by 1,771
Analysis of small-angle scattering data from colloids and polymer solutions: modeling and least-squares fitting
Pedersen, J. S. (1997) · Advances in Colloid and Interface Science · doi:10.1016/s0001-8686(97)00312-6 · Cited by 1,653
Basic Linear Algebra Subprograms for Fortran Usage
Lawson, C. L., Hanson, R. J., Kincaid, D. R., & Krogh, F. T. (1979) · ACM Transactions on Mathematical Software · doi:10.1145/355841.355847 · Cited by 1,626
Self-citation
The singular value decomposition: Its computation and some applications
Klema, V. C., & Laub, A. J. (1980) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1980.1102314 · Cited by 1,635
Essentials of the self-organizing map
Kohonen, T. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.09.018 · Cited by 1,617
Algorithms and applications for approximate nonnegative matrix factorization
Berry, M. W., Browne, M., Langville, A. N., Pauca, V. P., & Plemmons, R. J. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.11.006 · Cited by 1,516
Cell2location maps fine-grained cell types in spatial transcriptomics
Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Elmentaite, R., Lomakin, A., Kedlian, V., Gayoso, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01139-4 · Cited by 1,279
Improving the full spectrum fitting method: accurate convolution with Gauss–Hermite functions
Cappellari, M. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw3020 · Cited by 1,324
The successive projections algorithm for variable selection in spectroscopic multicomponent analysis
de Araújo, M. C. U., Saldanha, T. C. B., Galvão, R. K. H., Yoneyama, T., Chame, H. C., & Visani, V. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00119-8 · Cited by 1,332
Using data mining to model and interpret soil diffuse reflectance spectra
Rossel, R. A. V., & Behrens, T. (2010) · Geoderma · doi:10.1016/j.geoderma.2009.12.025 · Cited by 1,305
Density functional theory methods for characterization of porous materials
Landers, J., Gor, G. Y., & Neimark, A. V. (2013) · Colloids and Surfaces A Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2013.01.007 · Cited by 1,242
Deep supervised, but not unsupervised, models may explain IT cortical representation
Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204
Experimental methods in chemical engineering: specific surface area and pore size distribution measurements—BET, BJH, and DFT
Bardestani, R., Patience, G. S., & Kaliaguine, S. (2019) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.23632 · Cited by 1,219
NLPQL: A fortran subroutine solving constrained nonlinear programming problems
Schittkowski, K. (1986) · Annals of Operations Research · doi:10.1007/bf02022087 · Cited by 1,157
A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB
Jaumot, J., Gargallo, R., de Juan, A., & Tauler, R. (2005) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2004.12.007 · Cited by 1,151
A Linear-Time Algorithm for Gaussian and Non-Gaussian Trait Evolution Models
Ho, L. S. T., & Ané, C. (2014) · Systematic Biology · doi:10.1093/sysbio/syu005 · Cited by 1,103
Segmentation through variable-order surface fitting
Besl, P. J., & Jain, R. (1988) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.3881 · Cited by 1,077
A numerically stable dual method for solving strictly convex quadratic programs
Goldfarb, D., & Idnani, A. (1983) · Mathematical Programming · doi:10.1007/bf02591962 · Cited by 1,067
Inversion of strong ground motion and teleseismic waveform data for the fault rupture history of the 1979 Imperial Valley, California, earthquake
Hartzell, S. H., & Heaton, T. H. (1983) · Bulletin of the Seismological Society of America · doi:10.1785/bssa07306a1553 · Cited by 1,056
Small-angle scattering studies of biological macromolecules in solution
Svergun, D. I., & Koch, M. H. J. (2003) · Reports on Progress in Physics · doi:10.1088/0034-4885/66/10/r05 · Cited by 1,008
Penalized Regressions: The Bridge versus the Lasso
Fu, W. J. (1998) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1998.10474784 · Cited by 1,096
Linear Smoothers and Additive Models
Buja, A., Hastie, T. J., & Tibshirani, R. J. (1989) · The Annals of Statistics · doi:10.1214/aos/1176347115 · Cited by 998
Continuous probabilistic transform for voice conversion
Stylianou, Y., Cappé, O., & Moulines, É. (1998) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.661472 · Cited by 933
Nonmetric Individual Differences Multidimensional Scaling: An Alternating Least Squares Method with Optimal Scaling Features
Takane, Y., Young, F. W., & de Leeuw, J. (1977) · Psychometrika · doi:10.1007/bf02293745 · Cited by 951
Revisiting the Miles and Snow strategic framework: uncovering interrelationships between strategic types, capabilities, environmental uncertainty, and firm performance
DeSarbo, W. S., Benedetto, C. A. D., Song, M., & Sinha, I. (2004) · Strategic Management Journal · doi:10.1002/smj.431 · Cited by 911
The pMELTS: A revision of MELTS for improved calculation of phase relations and major element partitioning related to partial melting of the mantle to 3 GPa
Ghiorso, M. S., Hirschmann, M. M., Reiners, P. W., & Kress, V. C. (2002) · Geochemistry Geophysics Geosystems · doi:10.1029/2001gc000217 · Cited by 891
Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
Kim, H., & Park, H. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm134 · Cited by 865
A fast non-negativity-constrained least squares algorithm
Bro, R., & de Jong, S. (1997) · Journal of Chemometrics · doi:10.1002/(sici)1099-128x(199709/10)11:5 · Cited by 909
Fault Slip Distribution of the 1999 Mw 7.1 Hector Mine, California, Earthquake, Estimated from Satellite Radar and GPS Measurements
Jónsson, S. (2002) · Bulletin of the Seismological Society of America · doi:10.1785/0120000922 · Cited by 873
Quenched solid density functional theory and pore size analysis of micro-mesoporous carbons
Neimark, A. V., Lin, Y., Ravikovitch, P. I., & Thommes, M. (2009) · Carbon · doi:10.1016/j.carbon.2009.01.050 · Cited by 869
Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise
Kumaresan, R., & Tufts, D. W. (1982) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1982.1163974 · Cited by 870
The Hat Matrix in Regression and ANOVA
Hoaglin, D. C., & Welsch, R. E. (1978) · The American Statistician · doi:10.1080/00031305.1978.10479237 · Cited by 922
Fitting parameterized three-dimensional models to images
Lowe, D. G. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.134043 · Cited by 838
The truncatedSVD as a method for regularization
Hansen, P. C. (1987) · BIT Numerical Mathematics · doi:10.1007/bf01937276 · Cited by 830
Optimal infinite-horizon feedback laws for a general class of constrained discrete-time systems: Stability and moving-horizon approximations
Keerthi, S. S., & Gilbert, É. (1988) · Journal of Optimization Theory and Applications · doi:10.1007/bf00938540 · Cited by 828
PROPER ORTHOGONAL DECOMPOSITION AND ITS APPLICATIONS—PART I: THEORY
Liang, Y., Lee, H. P., Lim, S. P., et al. (2002) · Journal of Sound and Vibration · doi:10.1006/jsvi.2001.4041 · Cited by 822
Approximation Theory and Methods.
Wahlbin, L. B., & Powell, M. J. D. (1982) · Mathematics of Computation · doi:10.2307/2007351 · Cited by 805
Better Subset Regression Using the Nonnegative Garrote
Breiman, L. (1995) · Technometrics · doi:10.2307/1269730 · Cited by 1,045
The Multilinear Engine: A Table-Driven, Least Squares Program for Solving Multilinear Problems, including the n-Way Parallel Factor Analysis Model
Paatero, P. (1999) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1999.10474853 · Cited by 968
NLPQL: A fortran subroutine solving constrained nonlinear programming problems
Schittkowski, K. (1986) · Annals of Operations Research · doi:10.1007/bf02739235 · Cited by 754
T1–T2 Correlation Spectra Obtained Using a Fast Two-Dimensional Laplace Inversion
Song, Y., Venkataramanan, L., Hürlimann, M. D., et al. (2002) · Journal of Magnetic Resonance · doi:10.1006/jmre.2001.2474 · Cited by 753
The L-curve and its use in the numerical treatment of inverse problems
Hansen, P. C. (2000) · Cited by 803
Algorithmic modifications extending MELTS to calculate subsolidus phase relations
Asimow, P. D., & Ghiorso, M. S. (1998) · American Mineralogist · doi:10.2138/am-1998-9-1022 · Cited by 742
Fast, recursive-least-squares transversal filters for adaptive filtering
Cioffi, J. M., & Kailath, T. (1984) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1984.1164334 · Cited by 732
Synthesizing realistic facial expressions from photographs
Pighin, F. P., Hecker, J., Lischinski, D., Szeliski, R., & Salesin, D. (2006) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1185657.1185859 · Cited by 723
Unified Approach to Pore Size Characterization of Microporous Carbonaceous Materials from N2, Ar, and CO2 Adsorption Isotherms
Ravikovitch, P. I., Vishnyakov, A., Russo, R., & Neimark, A. V. (2000) · Langmuir · doi:10.1021/la991011c · Cited by 707
Efficient Algorithms for Computing a Strong Rank-Revealing QR Factorization
Gu, M., & Eisenstat, S. C. (1996) · SIAM Journal on Scientific Computing · doi:10.1137/0917055 · Cited by 700
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST
Long, Y., Ang, K. S., Li, M., Chong, K. L. K., Sethi, R., Zhong, C., Xu, H., Ong, Z., Sachaphibulkij, K., Chen, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36796-3 · Cited by 700
Chemical mass transfer in magmatic processes
Ghiorso, M. S., & Carmichael, I. S. E. (1985) · Contributions to Mineralogy and Petrology · doi:10.1007/bf00378255 · Cited by 687
Review of receptor modeling methods for source apportionment
Hopke, P. K. (2016) · Journal of the Air & Waste Management Association · doi:10.1080/10962247.2016.1140693 · Cited by 678
MutationalPatterns: comprehensive genome-wide analysis of mutational processes
Blokzijl, F., Janssen, R., van Boxtel, R., & Cuppen, E. (2018) · Genome Medicine · doi:10.1186/s13073-018-0539-0 · Cited by 660
Spatial Models of Parliamentary Voting
Poole, K. T. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511614644 · Cited by 629
Multiscale coarse graining of liquid-state systems
Izvekov, S., & Voth, G. A. (2005) · The Journal of Chemical Physics · doi:10.1063/1.2038787 · Cited by 656
Matrix Mathematics: Theory, Facts, and Formulas with Application to Linear Systems Theory
Bernstein, D. S. (2005) · Cited by 632
Untitled work
(2003) · doi:10.1175/1520-0426(2003)020
An H2O–CO2 mixed fluid saturation model compatible with rhyolite-MELTS
Ghiorso, M. S., & Gualda, G. A. R. (2015) · Contributions to Mineralogy and Petrology · doi:10.1007/s00410-015-1141-8 · Cited by 645
A REVIEW OF LEAST‐SQUARES INVERSION AND ITS APPLICATION TO GEOPHYSICAL PROBLEMS*
Lines, L. R., & Treitel, S. (1984) · Geophysical Prospecting · doi:10.1111/j.1365-2478.1984.tb00726.x · Cited by 636
Quantitative interpretation of NMR relaxation data
Whittall, K. P., & MacKay, A. L. (1989) · Journal of Magnetic Resonance (1969) · doi:10.1016/0022-2364(89)90011-5 · Cited by 633
Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3)--The Time-Independent Model
Field, E. H., Arrowsmith, R., Biasi, G. P., et al. (2014) · Bulletin of the Seismological Society of America · doi:10.1785/0120130164 · Cited by 633
Parameter estimation for differential equations: a generalized smoothing approach
Ramsay, J. O., Hooker, G., Campbell, D., & Cao, J. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00610.x · Cited by 613
The construction of movement by the spinal cord
Tresch, M. C., Saltiel, P., & Bizzi, E. (1999) · Nature Neuroscience · doi:10.1038/5721 · Cited by 634
GeoMF
Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., & Rui, Y. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623638 · Cited by 609
End-member modeling of compositional data: Numerical-statistical algorithms for solving the explicit mixing problem
Weltje, G. J. (1997) · Mathematical Geosciences · doi:10.1007/bf02775085 · Cited by 611
[8] Singular value decomposition: Application to analysis of experimental data
Henry, E. R., & Hofrichter, J. (1992) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/0076-6879(92)10010-b · Cited by 613
ATSAS2.1, a program package for small-angle scattering data analysis
Konarev, P. V., Petoukhov, M. V., Волков, В. В., & Svergun, D. I. (2006) · Journal of Applied Crystallography · doi:10.1107/s0021889806004699 · Cited by 606
Social Media Mining
Zafarani, R., Abbasi, M. A., & Liu, H. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139088510 · Cited by 620
Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method
Kim, H., & Park, H. (2008) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/07069239x · Cited by 574
Estimation of Inertial Parameters of Manipulator Loads and Links
Atkeson, C. G., An, C. H., & Hollerbach, J. M. (1986) · The International Journal of Robotics Research · doi:10.1177/027836498600500306 · Cited by 590
Physicochemical Profiling (Solubility, Permeability and Charge State)
Avdeef, A. (2001) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026013395100 · Cited by 595
The fine-scale genetic structure of the British population
Leslie, S., WINNEY, B. J., Hellenthal, G., Davison, D., Boumertit, A., Day, T., Hutnik, K., Røyrvik, E. C., Cunliffe, B. W., Lawson, D. J., et al. (2015) · Nature · doi:10.1038/nature14230 · Cited by 572
Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm
Gorodnitsky, I. F., George, J. S., & Rao, B. D. (1995) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(95)00107-a · Cited by 567
Fast online deconvolution of calcium imaging data
Friedrich, J. H., Zhou, P., & Paninski, L. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005423 · Cited by 533
Convex Optimization in Signal Processing and Communications
Palomar, D. P., & Eldar, Y. C. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511804458 · Cited by 570
Towards a Generalized Singular Value Decomposition
Paige, C. C., & Saunders, M. A. (1981) · SIAM Journal on Numerical Analysis · doi:10.1137/0718026 · Cited by 555
Modeling Information Diffusion in Implicit Networks
Yang, J., & Leskovec, J. (2010) · Proceedings · doi:10.1109/icdm.2010.22 · Cited by 563

Authors: Lawson, C. L., & Hanson, R. J.
Affiliations: California Institute of Technology (US)
Venue: Society for Industrial and Applied Mathematics eBooks
Publisher: Society for Industrial and Applied Mathematics
Published: 1995-01-01
DOI: 10.1137/1.9781611971217
OpenAlex: W2075665712
Cited by: 7,103
References: 0
Citations by year: 2026: 111 · 2025: 129 · 2024: 154 · 2023: 165 · 2022: 183 · 2021: 224 · 2020: 217 · 2019: 237 · 2018: 250 · 2017: 216 · 2016: 210 · 2015: 241 · 2014: 270 · 2013: 265 · 2012: 237
FWCI: 45.68
Type: book
Language: en
Open access: closed
Locations: Society for Industrial and Applied Mathematics eBooks (publishedVersion)
CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Matrix Theory and Algorithms
Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
Keywords: least-squares problems, linear least squares, nonlinear least squares, numerical linear algebra, iterative methods, singular value decomposition, QR decomposition, condition number, ill-conditioned problems

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Solving Least Squares Problems

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks