Ilse Ipsen

MONTE CARLO METHODS FOR ESTIMATING THE DIAGONAL OF A REAL SYMMETRIC MATRIX

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 44(1), 240–269.

A projector-based approach to quantifying total and excess uncertainties for sketched linear regression

Chi, J. T., & Ipsen, I. C. F. (2021, August 11). INFORMATION AND INFERENCE-A JOURNAL OF THE IMA, Vol. 8.

Multiplicative perturbation bounds for multivariate multiple linear regression in Schatten p-norms

LINEAR ALGEBRA AND ITS APPLICATIONS, 624, 87–102.

Probabilistic Iterative Methods for Linear Systems

Journal of Machine Learning Research. http://jmlr.org/papers/v22/21-0031.html

SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data

FRONTIERS IN GENETICS, 12.

SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-based GxE Tests in Biobank Data

Probabilistic Error Analysis for Inner Products

SIAM Journal on Matrix Analysis and Applications, 41(4), 1726–1741.

A Bayesian conjugate gradient method

Bayesian Anal., 14(3), 937–1012.

Editorial: special edition on probabilistic numerics

Girolami, M., Ipsen, I. C. F., Oates, C. J., Owen, A. B., & Sullivan, T. J. (2019, November). STATISTICS AND COMPUTING, Vol. 29, pp. 1181–1183.

LOW-RANK MATRIX APPROXIMATIONS DO NOT NEED A SINGULAR VALUE GAP

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 40(1), 299–319.

Probabilistic linear solvers: a unifying view

STATISTICS AND COMPUTING, 29(6), 1249–1263.

A PROBABILISTIC SUBSPACE BOUND WITH APPLICATION TO ACTIVE SUBSPACES

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 39(3), 1208–1220.

Eigenvector Continuation with Subspace Learning

Physical Review Letters, 121(3).

STRUCTURAL CONVERGENCE RESULTS FOR APPROXIMATION OF DOMINANT SUBSPACES FROM BLOCK KRYLOV SPACES

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 39(2), 567–586.

Randomized matrix-free trace and log-determinant estimators

NUMERISCHE MATHEMATIK, 137(2), 353–395.

RandNLA, Pythons, and the CUR for your data problems: Reporting from G2S3 in Delphi

SIAM News, 49(1), 7–8.

CONDITIONING OF LEVERAGE SCORES AND COMPUTATION BY QR DECOMPOSITION

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 36(3), 1143–1163.

Preface

Linear Algebra and Its Applications, 464, 1–2.

RANDOMIZED APPROXIMATION OF THE GRAM MATRIX: EXACT COMPUTATION AND PROBABILISTIC BOUNDS

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 36(1), 110–137.

THE EFFECT OF COHERENCE ON SAMPLING FROM MATRICES WITH ORTHONORMAL COLUMNS, AND PRECONDITIONED LEAST SQUARES PROBLEMS

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 35(4), 1490–1520.

Research spotlights

SIAM Review, 54(1), 119–120.

COMPUTING CHARACTERISTIC POLYNOMIALS FROM EIGENVALUES

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 32(1), 90–114.

ERGODICITY COEFFICIENTS DEFINED BY VECTOR NORMS

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 32(1), 153–200.

Expository Research Papers

SIAM Review, 53(1), 69–69.

Expository Research Papers

SIAM Review, 53(2), 289–289.

Expository Research Papers

SIAM Review, 53(4), 721–721.

Expository Research Papers

SIAM Review, 53(3), 503–503.

Expository research papers

SIAM Review, Vol. 53. http://www.scopus.com/inward/record.url?eid=2-s2.0-84856706336&partnerID=MN8TOARS

IMPORTANCE SAMPLING FOR A MONTE CARLO MATRIX MULTIPLICATION ALGORITHM, WITH APPLICATION TO INFORMATION RETRIEVAL

SIAM JOURNAL ON SCIENTIFIC COMPUTING, 33(4), 1689–1706.

RANK-DEFICIENT NONLINEAR LEAST SQUARES PROBLEMS AND SUBSET SELECTION

SIAM JOURNAL ON NUMERICAL ANALYSIS, 49(3), 1244–1266.

Expository Research Papers

SIAM Review, 52(3), 453–453.

Expository Research Papers

SIAM Review, 52(2), 267–267.

Expository Research Papers

SIAM Review, 52(4), 675–675.

Expository Research Papers

SIAM Review, 52(1), 55–55.

Expository research papers

SIAM Review, Vol. 52. http://www.scopus.com/inward/record.url?eid=2-s2.0-79952956707&partnerID=MN8TOARS

Rank-deficient and ill-conditioned nonlinear least squares problems

Proceedings of the 2010 East Asian SIAM Conference. Presented at the East Asian Society of Industrial and Applied Mathematics Conference.

The Eigenproblem and Invariant Subspaces: Perturbation Theory

In G.W. Stewart (pp. 71–93).

Preface to the 14th ILAS Conference Proceedings Shanghai 2007

Linear Algebra and Its Applications, 430(5-6).

Problems and Techniques

SIAM Review, 51(2), 337–337.

Problems and Techniques

SIAM Review, 51(3), 543–543.

Problems and Techniques

SIAM Review, 51(4), 705–705.

Problems and Techniques

Ipsen, I. (2009, February 5). SIAM Review, Vol. 51, pp. 127–127.

Problems and techniques

SIAM Review, Vol. 51. http://www.scopus.com/inward/record.url?eid=2-s2.0-59749092679&partnerID=MN8TOARS

REFINED PERTURBATION BOUNDS FOR EIGENVALUES OF HERMITIAN AND NON-HERMITIAN MATRICES

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, Vol. 31, pp. 40–53.

SPECIAL ISSUE ON ACCURATE SOLUTION OF EIGENVALUE PROBLEMS

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, Vol. 31, pp. VII-VII.

Special issue on accurate solution of eigenvalue problems

SIAM Journal on Matrix Analysis and Applications, 31(1). http://www.scopus.com/inward/record.url?eid=2-s2.0-73649094469&partnerID=MN8TOARS

ORDINAL RANKING FOR GOOGLE'S PAGERANK

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 30(4), 1677–1696.

PERTURBATION BOUNDS FOR DETERMINANTS AND CHARACTERISTIC POLYNOMIALS

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 30(2), 762–776.

Problems and Techniques

SIAM Review, 50(2), 273–273.

Problems and Techniques

Ipsen, I. (2008, January). SIAM Review, Vol. 50, pp. 35–35.

Problems and Techniques

SIAM Review, 50(4), 721–721.

Problems and Techniques

SIAM Review, 50(3), 483–483.

Problems and techniques

SIAM Review, Vol. 50. http://www.scopus.com/inward/record.url?eid=2-s2.0-50949105062&partnerID=MN8TOARS

Condition estimates for pseudo-arclength continuation

SIAM JOURNAL ON NUMERICAL ANALYSIS, 45(1), 263–276.

First SIAG linear algebra school slated for July 2008

Ipsen, I. C. F. (2007, November 3). SIAM News, 40(9).

Pagerank computation, with special attention to dangling nodes

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 29(4), 1281–1296.

Problems and Techniques

SIAM Review, 49(4), 593–593.

Problems and Techniques

Ipsen, I. (2007, January). SIAM Review, Vol. 49, pp. 209–209.

Problems and Techniques

SIAM Review, 49(3), 419–420.

Problems and Techniques

SIAM Review, 49(1), 33–34.

Problems and techniques

SIAM Review, Vol. 49. http://www.scopus.com/inward/record.url?eid=2-s2.0-34249999151&partnerID=MN8TOARS

Problems and techniques: Introduction

SIAM Review, 49(4). http://www.scopus.com/inward/record.url?eid=2-s2.0-37249005235&partnerID=MN8TOARS

Problems and techniques: Introduction

SIAM Review, Vol. 49. http://www.scopus.com/inward/record.url?eid=2-s2.0-37249005235&partnerID=MN8TOARS

Communicating Applied Mathematics: Four Examples

SIAM Review, 48(2), 359–389.

Convergence analysis of a PageRank updating algorithm by Langville and Meyer

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 27(4), 952–967.

Mathematical properties and analysis of Google’s PageRank

Boletin De La Sociedad Espanola De Matematica Aplicada, 34, 191–196.

New ideas for SIAM conferences from Europe

Ipsen, I. C. F. (2006, December). SIAM News, 39(10), 5.

Problems and Techniques

SIAM Review, 48(1), 41–42.

Problems and Techniques

SIAM Review, 48(3), 485–485.

Problems and Techniques

SIAM Review, 48(4), 679–680.

Problems and Techniques

SIAM Review, 48(2), 305–305.

Problems and techniques

SIAM Review, Vol. 48, pp. 41–42. http://www.scopus.com/inward/record.url?eid=2-s2.0-33644586567&partnerID=MN8TOARS

Problems and techniques: Introduction

SIAM Review, 48(2), 485–486. http://www.scopus.com/inward/record.url?eid=2-s2.0-33744920863&partnerID=MN8TOARS

Problems and techniques: Introduction

SIAM Review, Vol. 48, pp. 485–486. http://www.scopus.com/inward/record.url?eid=2-s2.0-33744920863&partnerID=MN8TOARS

Special issue on accurate solution of eigenvalue problems

SIAM Journal on Matrix Analysis and Applications, 28(4). http://www.scopus.com/inward/record.url?eid=2-s2.0-35348933724&partnerID=MN8TOARS

Special issue on accurate solution of eigenvalue problems

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, Vol. 28, pp. IX-IX.

How to get SIAM cooperation for your meeting

SIAM News, 38(2).

Problems and Techniques

SIAM Review, 47(4), 707–707.

Problems and techniques

SIAM Review, Vol. 47. http://www.scopus.com/inward/record.url?eid=2-s2.0-28244444106&partnerID=MN8TOARS

Why aren’t SIAM conferences cheaper?

Ipsen, I. C. F. (2005, January). SIAM News, 38(1), 1.

Accurate eigenvalues for fast trains

Ipsen, I. C. F. (2004, November). SIAM News, 37(9), 1–2.

A note on unifying absolute and relative perturbation bounds

Ipsen, I. C. F. (2003, January 1). LINEAR ALGEBRA AND ITS APPLICATIONS, Vol. 358, pp. 239–253.

Inclusion regions for matrix eigenvalues

Beattie, C., & Ipsen, I. C. F. (2003, January 1). LINEAR ALGEBRA AND ITS APPLICATIONS, Vol. 358, pp. 281–291.

Zone determinant expansions for nuclear lattice simulations

PHYSICAL REVIEW C, 68(6).

A note on preconditioning nonsymmetric matrices

SIAM JOURNAL ON SCIENTIFIC COMPUTING, 23(3), 1050–1051.

SIAG/LA and ILAS mark twenty years of progress at joint applied linear algebra meeting

SIAM News, 34(1).

Stability Radius and Optimal Scaling of Discrete-Time Periodic Systems

IFAC Proceedings Volumes, 34(12), 179–182.

A note on a certain class of preconditioners for symmetric indefinite linear systems

(Technical Report No. M&CT-TECH-00-005). Mathematics & Computing Technology, Phantom Works Division, The Boeing Company.

Absolute and relative perturbation bounds for invariant subspaces of matrices

Ipsen, I. C. F. (2000, April 15). LINEAR ALGEBRA AND ITS APPLICATIONS, Vol. 309, pp. 45–56.

An overview of relative sin Theta theorems for invariant subspaces of complex matrices

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 123(1-2), 131–153.

Expressions and bounds for the GMRES residual

BIT, 40(3), 524–535.

A different approach to bounding the minimal residual norm in Krylov methods

(Technical Report No. CRSC-TR98-19). Raleigh, NC: Center for Research in Scientific Computation, Department of Mathematics, North Carolina State University.

A note on the field of values of non-normal matrices

(Technical Report No. CRSC-TR98-26). Raleigh, NC: Center for Research in Scientific Computation, Department of Mathematics, North Carolina State University.

If a matrix has a single eigenvalue, how sensitive is this eigenvalue?

(Technical Report No. CRSC-TR98-8). Raleigh, NC: Center for Research in Scientific Computation, Department of Mathematics, North Carolina State University.

Relative perturbation results for eigenvalues and eigenvectors of diagonalisable matrices

BIT, 38(3), 502–509.

Relative perturbation results for matrix eigenvalues and singular values

Acta Numerica, 7, 151–201.

The idea behind Krylov methods

AMERICAN MATHEMATICAL MONTHLY, 105(10), 889–899.

The lack of influence of the right-hand side on the accuracy of linear system solution

SIAM Journal on Scientific Computing, 20(1), 203–227. http://www.scopus.com/inward/record.url?eid=2-s2.0-0032131636&partnerID=MN8TOARS

The lack of influence of the right-hand side on the accuracy of linear system solution

SIAM JOURNAL ON SCIENTIFIC COMPUTING, 20(1), 203–227.

Three absolute perturbation bounds for matrix eigenvalues imply relative bounds

SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 20(1), 149–158.

Computing an eigenvector with inverse iteration

SIAM REVIEW, 39(2), 254–291.

If a matrix has a single eigenvalue, how sensitive is this eigenvalue?

(Technical Report No. CRSC-TR97-20). Raleigh, NC: Center for Research in Scientific Computation, Department of Mathematics, North Carolina State University.

A history of inverse iteration

In H. Wielandt, B. Huppert, & H. Schneider (Eds.), Mathematische Werke. Vol. 2, Linear algebra and analysis : Mathematical works (pp. 464–472). Berlin: Walter de Gruyter.

Convergence Estimates for Solution of Integral Equations with GMRES

Journal of Integral Equations and Applications, 8(1), 19–34.

GMRES and the minimal polynomial

BIT Numerical Mathematics, 36(4), 664–675.

Helmut Wielandt’s contributions to the numerical solution of complex eigenvalue problems

In B. Huppert & H. Schneider (Eds.), Helmut Wielandt, Mathematische Werke, Mathematical Works, volume 2: Linear Algebra and Analysis (pp. 453–463). Berlin: Walter de Gruyter.

Analysis of a QR Algorithm for Computing Singular Values

SIAM Journal on Matrix Analysis and Applications, 16(2), 520–535.

On the Sensitivity of Solution Components in Linear Systems of Equations

SIAM Journal on Matrix Analysis and Applications, 16(1), 93–112.

Relative Perturbation Techniques for Singular Value Problems

SIAM Journal on Numerical Analysis, 32(6), 1972–1988.

The Angle Between Complementary Subspaces

The American Mathematical Monthly, 102(10), 904–911.

A divide and conquer algorithm for computing singular values

Zeitschrift Für Angewandte Mathematik Und Mechanik, 74(6), 532–534.

Backward errors for eigenvalue and singular value decompositions

Numerische Mathematik, 68(2), 215–223.

On Rank-Revealing Factorisations

SIAM Journal on Matrix Analysis and Applications, 15(2), 592–622.

On the singular value decomposition of triangular matrices

In J. Er-xiong (Ed.), Numerical algebra : proceedings of '92 Shanghai International Numerical Algebra and its Applications Conference (pp. 85–89). Shanghai: China Science and Technology Press.

Relative perturbation bounds for eigenspaces and singular vector subspaces

In J. G. Lewis (Ed.), Proceedings of the Fifth SIAM Conference on Applied Linear Algebra (pp. 62–65). Philadelphia: SIAM.

Uniform Stability of Markov Chains

SIAM Journal on Matrix Analysis and Applications, 15(4), 1061–1074.

Improving the Accuracy of Inverse Iteration

SIAM Journal on Scientific and Statistical Computing, 13(2), 550–572.

Perturbation Theory for the Solution of Systems of Linear Equations

Some Remarks on the Generalised Bareiss and Levinson Algorithms

In Numerical Linear Algebra, Digital Signal Processing and Parallel Algorithms (pp. 189–214).

From Bareiss' algorithm to the stable computation of partial correlations

In Advances in Parallel Computing (Vol. 1, pp. 53–91).

Solving the Symmetric Tridiagonal Eigenvalue Problem on the Hypercube

SIAM Journal on Scientific and Statistical Computing, 11(2), 203–229.

A Gray code scheme for local uniform mesh refinement on hypercubes

In Parallel Processing for Scientific Computing (pp. 202–206). Philadelphia: Society for Industrial and Applied Mathematics.

From Bareiss' algorithm to the stable computation of partial correlations

Journal of Computational and Applied Mathematics, 27(1-2), 53–91.

Parallel computation of algorithms with uniform dependences

In J. Dongarra, P. Messina, D. C. Sorensen, & R. G. Voigt (Eds.), Proceedings of the Fourth SIAM Conference on Parallel Processing for Scientific Computing (pp. 319–326). Philadelphia: Society for Industrial and Applied Mathematics.

Recursive mesh refinement on hypercubes

BIT, 29(2), 186–211.

SAGA and CONDENSE: a two-phase approach for the implementation of recurrence equations on multiprocessor architectures

[1988] Proceedings of the Twenty-First Annual Hawaii International Conference on System Sciences. Volume I: Architecture Track, 126–130.

Systolic Algorithms for the Parallel Solution of Dense Symmetric Positive-Definite Toeplitz Systems

In The IMA Volumes in Mathematics and Its Applications (pp. 85–108).

The Cholesky factorization, Schur complements, correlation coefficients, angles between vectors, and the QR factorization

(Research Report No. 607). New Haven, Connecticut: Department of Computer Science, Yale University.

A comparison of Cuppen’s method and multisection for the solution of tridiagonal eigenvalue problems on the hypercube

In Advances in Computer Methods for Partial Differential Equations VI (pp. 425–430). The Institute for Mathematics and Computer Science.

Computing partial correlations from the data matrix

(Research Report No. 541). New Haven, Connecticut: Department of Computer Science, Yale University.

Efficient systolic arrays for the solution of Toeplitz systems: An illustration of a methodology for the construction of systolic architectures in VLSI

In Systolic Arrays (pp. 37–46). Adam Hilger.

ParLance: A para-functional programming environment for parallel and distributed computing

(Research Report No. 524). New Haven, Connecticut: Department of Computer Science, Yale University.

Scaled Givens Rotations for the Solution of Linear Least Squares Problems on Systolic Arrays

SIAM Journal on Scientific and Statistical Computing, 8(5), 716–733.

Two methods for solving the symmetric tridiagonal eigenvalue problem on the hypercube

In Hypercube Multiprocessors (pp. 627–638). Society for Industrial and Applied Mathematics.

Complexity of dense-linear-system solution on a multiprocessor ring

Linear Algebra and Its Applications, 77(C), 205–239.

Design Methodology For Systolic Arrays

In J. M. Speiser (Ed.), Advanced Algorithms and Architectures for Signal Processing I (Vol. 696, pp. 245–259).

Parallel solution of symmetric positive definite systems with hyperbolic rotations

Linear Algebra and Its Applications, 77(C), 75–111.

Systolic array synthesis: Computability and time cones

In Parallel Algorithms and Architectures (pp. 295–312). Amsterdam: North-Holland Publishing.

Systolic implementation of a Toeplitz system solver

(Research Report No. 8607). New Haven, Connecticut: Department of Electrical Engineering, Yale University.

The Impact of Parallel Architectures on The Solution of Eigenvalue Problems

In Large Scale Eigenvalue Problems, Proceedings of the IBM Europe Institute Workshop on Large Scale Eigenvalue Problems (Vol. 127, pp. 37–49).

An illustration of a methodology for the construction of efficient systolic architectures in VLSI

Proceedings of the Second International Symposium on VLSI Technology, Systems and Applications, 268–273.

How to embed trees in hypercubes

(Research Report No. 443). New Haven, Connecticut: Department of Computer Science, Yale University.

A parallel QR method using fast Givens’ rotations

(Research Report No. 299). New Haven, Connecticut: Department of Computer Science, Yale University.

Singular Value Decomposition With Systolic Arrays

In K. Bromley (Ed.), Real-Time Signal Processing VII.

Systolic Networks for Orthogonal Decompositions

SIAM Journal on Scientific and Statistical Computing, 4(2), 261–269.

Systolic networks for orthogonal equivalence transformations and their applications

In P. Penfield (Ed.), Proceedings of the Conference on Advanced Research in VLSI (pp. 113–122). Norwood, MA: Artech House, Inc.