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ORCIDLars Eldén
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2020 – today
- 2024
[j23]Lars Eldén
:
Multiway Spectral Graph Partitioning: Cut Functions, Cheeger Inequalities, and a Simple Algorithm. SIAM J. Matrix Anal. Appl. 45(1): 112-133 (2024)- 2023
- [i5]Lars Eldén:
Multiway Spectral Graph Partitioning: Cut Functions, Cheeger Inequalities, and a Simple Algorithm. CoRR abs/2302.03615 (2023) - 2022
- [j22]Lars Eldén, Maryam Dehghan:
A Krylov-Schur-like method for computing the best rank-(r1, r2, r3) approximation of large and sparse tensors. Numer. Algorithms 91(3): 1315-1347 (2022) - [j21]Lars Eldén, Maryam Dehghan:
Spectral partitioning of large and sparse 3-tensors using low-rank tensor approximation. Numer. Linear Algebra Appl. 29(5) (2022) - 2020
- [i4]Lars Eldén, Maryam Dehghan:
A Krylov-Schur like method for computing the best rank-(r1, r2, r3) approximation of large and sparse tensors. CoRR abs/2012.07595 (2020) - [i3]Lars Eldén, Maryam Dehghan:
Spectral Partitioning of Large and Sparse Tensors using Low-Rank Tensor Approximation. CoRR abs/2012.07689 (2020) - [i2]Lars Eldén, Maryam Dehghan:
Analyzing Large and Sparse Tensor Data using Spectral Low-Rank Approximation. CoRR abs/2012.07754 (2020)
2010 – 2019
- 2019
- [j20]Lars Eldén, Salman Ahmadi-Asl:
Solving bilinear tensor least squares problems and application to Hammerstein identification. Numer. Linear Algebra Appl. 26(2) (2019) - 2014
- [j19]Mansoor Rezghi, Seyed Mohammad Hosseini, Lars Eldén:
Best Kronecker Product Approximation of The Blurring Operator in Three Dimensional Image Restoration Problems. SIAM J. Matrix Anal. Appl. 35(3): 1086-1104 (2014) - [j18]Zohreh Ranjbar, Lars Eldén:
Solving an Ill-Posed Cauchy Problem for a Two-Dimensional Parabolic PDE with Variable Coefficients Using a Preconditioned GMRES Method. SIAM J. Sci. Comput. 36(5) (2014) - 2013
- [j17]Lars Eldén, Magnus Merkel, Lars Ahrenberg, Martin Fagerlund:
Computing Semantic Clusters by Semantic Mirroring and Spectral Graph Partitioning. Math. Comput. Sci. 7(3): 293-313 (2013) - 2012
- [j16]Lars Eldén, Valeria Simoncini:
Solving Ill-Posed Linear Systems with GMRES and a Singular Preconditioner. SIAM J. Matrix Anal. Appl. 33(4): 1369-1394 (2012) - 2011
- [j15]Lars Eldén, Berkant Savas:
Perturbation Theory and Optimality Conditions for the Best Multilinear Rank Approximation of a Tensor. SIAM J. Matrix Anal. Appl. 32(4): 1422-1450 (2011) - [i1]Lars Eldén, Andreas Frommer:
Data Mining, Networks and Dynamics (Dagstuhl Seminar 11451). Dagstuhl Reports 1(11): 23-38 (2011) - 2010
- [j14]Jesse L. Barlow, Lars Eldén, Paolo Foschi:
3rd Special issue on matrix computations and statistics. Comput. Stat. Data Anal. 54(12): 3379-3380 (2010) - [c3]Martin Fagerlund, Magnus Merkel, Lars Eldén, Lars Ahrenberg:
Computing Word Senses by Semantic Mirroring and Spectral Graph Partitioning. TextGraphs@ACL 2010: 103-107
2000 – 2009
- 2009
- [j13]Lars Eldén, Berkant Savas:
A Newton-Grassmann Method for Computing the Best Multilinear Rank-(r1, r2, r3) Approximation of a Tensor. SIAM J. Matrix Anal. Appl. 31(2): 248-271 (2009) - 2007
- [b1]Lars Eldén:
Matrix methods in data mining and pattern recognition. Fundamentals of algorithms 4, SIAM 2007, ISBN 978-0-89871-626-9, pp. I-X, 1-224 - [j12]Berkant Savas, Lars Eldén:
Handwritten digit classification using higher order singular value decomposition. Pattern Recognit. 40(3): 993-1003 (2007) - [c2]Hyunsoo Kim, Haesun Park, Lars Eldén:
Non-negative Tensor Factorization Based on Alternating Large-scale Non-negativity-constrained Least Squares. BIBE 2007: 1147-1151 - 2005
- [j11]Lars Eldén, Berkant Savas:
The maximum likelihood estimate in reduced-rank regression. Numer. Linear Algebra Appl. 12(8): 731-741 (2005) - 2004
- [j10]Lars Eldén:
Partial least-squares vs. Lanczos bidiagonalization - I: analysis of a projection method for multiple regression. Comput. Stat. Data Anal. 46(1): 11-31 (2004) - 2002
- [j9]Eva Lundström, Lars Eldén:
Adaptive Eigenvalue Computations Using Newton's Method on the Grassmann Manifold. SIAM J. Matrix Anal. Appl. 23(3): 819-839 (2002) - 2000
- [j8]Lars Eldén, Fredrik Berntsson, Teresa Reginska:
Wavelet and Fourier Methods for Solving the Sideways Heat Equation. SIAM J. Sci. Comput. 21(6): 2187-2205 (2000) - [j7]Haesun Park, Lars Eldén:
Schur-Type Methods for Solving Least Squares Problems with Toeplitz Structure. SIAM J. Sci. Comput. 22(2): 406-430 (2000)
1990 – 1999
- 1999
- [j6]Lars Eldén, Haesun Park:
A Procrustes problem on the Stiefel manifold. Numerische Mathematik 82(4): 599-619 (1999) - 1998
- [j5]Achiya Dax, Lars Eldén:
Approximating minimum norm solutions of rank-deficient least squares problems. Numer. Linear Algebra Appl. 5(2): 79-99 (1998) - 1996
- [j4]Lars Eldén, Eva Sjöström:
Fast computation of the principal singular vectors of Toeplitz matrices arising in exponential data modelling. Signal Process. 50(1-2): 151-164 (1996) - 1995
- [j3]Haesun Park, Lars Eldén:
Downdating the Rank-Revealing URV Decomposition. SIAM J. Matrix Anal. Appl. 16(1): 138-155 (1995) - 1994
- [j2]Åke Björck, Haesun Park, Lars Eldén:
Accurate Downdating of Least Squares Solutions. SIAM J. Matrix Anal. Appl. 15(2): 549-568 (1994) - [j1]Lars Eldén, Haesun Park:
Block Downdating of Least Squares Solutions. SIAM J. Matrix Anal. Appl. 15(3): 1018-1034 (1994) - [c1]Haesun Park, Sabine Van Huffel, Lars Eldén:
Fast algorithms for exponential data modeling. ICASSP (4) 1994: 25-28
Coauthor Index
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