James Baglama

James Baglama Department of Mathematics and Applied Mathematical Sciences University of Rhode Island Kingston, Rhode Island 02881 Phone: 401.874.2709

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Fall 2024 MTH 215-02 (MWF) Fall 2024 MTH 215-03 (TTH) Fall 2024 MTH 471/571-01 (TTH)	URI Catalog	URI Academic Calendars
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Education

• Dec. 1997 Ph.D. Applied Mathematics Kent State University
  Advisors: Lothar Reichel and Arden Ruttan
• Dec. 1991 M.S. Mathematics Youngstown State University
  Advisor: John J. Buoni
• Aug. 1990 B.S. Mathematics Youngstown State University
  Advisor: J. Douglas Faires

Journal Papers (peer reviewed) and Software

James Baglama - Google Scholar
James Baglama - ResearchGate
James Baglama - GitHub
James Baglama - ORCID
 
All pdf files of papers made available below may differ slightly from the final versions published in the corresponding journal.

• J. Baglama, D. Calvetti and L. Reichel, Iterative methods for the computation of a few eigenvalues of a large symmetric matrix. BIT Numerical Mathematics, 36, (1996), pp.400-421. pdf file
   
  
• J. Baglama, D. Calvetti, L. Reichel, and A. Ruttan, Computation of a few small eigenvalues of a large matrix with application to liquid crystal modeling. Journal of Computational Physics, 146(1), (1998), pp.203-226. pdf file
   
  
• J. Baglama, D. Calvetti, and L. Reichel, Fast leja points. Electron. Trans. Numer. Anal, 7(124-140), (1998), pp.119-120. pdf file
   
  
• J. Baglama, D. Calvetti, G.H. Golub, and L. Reichel, Adaptively preconditioned GMRES algorithms. SIAM Journal on Scientific Computing, 20(1), (1998), pp.243-269. pdf file
   
  
• S.J. Jang, and J. Baglama, Qualitative behavior of a variable-yield simple food chain with an inhibiting nutrient. Mathematical biosciences, 164(1), (2000), pp.65-80. pdf file
   
  
• E.J. Allen, J. Baglama, and S.K. Boyd, Numerical approximation of the product of the square root of a matrix with a vector. Linear Algebra and its Applications, 310(1-3), (2000), pp.167-181. pdf file
     
  
• J. Baglama, Dealing with linear dependence during the iterations of the restarted block Lanczos methods. Numerical Algorithms, 25, (2000), pp.23-36. pdf file
     
  
• S.J. Jang and J. Baglama, A nutrient-prey-predator model with intratrophic predation. Applied mathematics and computation, 129(2-3), (2002), pp.517-536. pdf file
     
  
• J. Baglama, D. Calvetti, and L. Reichel, IRBL: An implicitly restarted block-Lanczos method for large-scale Hermitian eigenproblems. SIAM Journal on Scientific Computing, 24(5), (2003), pp.1650-1677. pdf file
     
  
• J. Baglama, D. Calvetti, and L. Reichel, Algorithm 827: irbleigs: A MATLAB program for computing a few eigenpairs of a large sparse Hermitian matrix. ACM Transactions on Mathematical Software (TOMS), 29(3), (2003), pp.337-348. pdf file
  
  • Click here to download the MATLAB code irbleigs.m.
  • Click here to download the MATLAB code irblsvds.m
  
  
• S.J. Jang and J. Baglama, Persistence in variable-yield nutrient-plankton models. Mathematical and computer modelling, 38(3-4), (2003), pp.281-298. pdf file
     
  
• S.J. Jang, J. Baglama, and P. Seshaiyer, Intratrophic predation in a simple food chain with fluctuating nutrient. Discrete Contin. Dyn. Syst. Ser. B, 5(2), (2005), pp.335-352. pdf file
     
  
• S.J. Jang and J. Baglama, Nutrient-plankton models with nutrient recycling. Computers and Mathematics with Applications, 49(2-3), (2005), pp.375-387. pdf file
     
  
• J. Baglama and L. Reichel, Augmented implicitly restarted Lanczos bidiagonalization methods. SIAM Journal on Scientific Computing, 27(1), (2005), pp.19-42. pdf file
  
  • Click here to download the MATLAB code irlba.m.
  • Click here for the R package on CRAN for the code irlba. Code is maintained by Bryan Lewis.
  • Click here for the R package on GitHub for the code irlba. Code is maintained by Bryan Lewis.
  • Click here for the Python package of irlba. Code is maintained by Bryan Lewis.
  • Click here for the C++ library for irlba. Code is maintained by Aaron Lun.
  
  
• S.J. Jang and J. Baglama, Droop models of nutrient-plankton interaction with intratrophic predation. Applied mathematics and computation, 169(2), (2005), pp.1106-1128. pdf file
     
  
• S.J. Jang, J. Baglama, and J. Rick, Nutrient-phytoplankton-zooplankton models with a toxin. Mathematical and Computer Modelling, 43(1-2), (2006), pp.105-118. pdf file
     
  
• J. Baglama and L. Reichel, Restarted block Lanczos bidiagonalization methods. Numerical Algorithms, 43(3), (2006), pp.251-272. pdf file
  
  • Click here to download the block MATLAB code irlbablk.m
  
  
• J. Baglama and L. Reichel, Decomposition methods for large linear discrete ill-posed problems. Journal of computational and applied mathematics, 198(2),(2007), pp.332-343. pdf file
     
  
• J. Baglama and L. Reichel, Augmented GMRES-type methods. Numerical Linear Algebra with Applications, 14(4), (2007), pp.337-350. pdf file
     
  
• J. Baglama, Augmented block householder Arnoldi method. Linear algebra and its applications, 429(10), (2008), pp.2315-2334. pdf file
  
  • Click here to download the MATLAB code ahbeigs.m
  
  
• S.J. Jang, J. Baglama, and J. Rick, Plankton-toxin interaction with a variable input nutrient. Journal of Biological Dynamics, 2(1), (2008), pp.14-30. pdf file
     
  
• S.J. Jang and J. Baglama, Continuous-time predator-prey models with parasites. Journal of Biological Dynamics, 3(1), (2009), pp.87-98. pdf file
     
  
• S.J. Jang, J. Baglama, and L. Wu, Random dispersal in a predator-prey-parasite system. Journal of Biological Systems, 18(04), (2010), pp.825-845. pdf file
     
  
• J. Baglama and L. Reichel, An implicitly restarted block Lanczos bidiagonalization method using Leja shifts. BIT Numerical Mathematics, 53, (2013), pp.285-310.
     
  
• J. Baglama, L. Reichel, and D. Richmond, An augmented LSQR method. Numerical Algorithms, 64, (2013), pp.263-293.
  
  • Click here to download the MATLAB code alsqr.m
  
  
• S.J. Jang, J. Baglama, and L. Wu, Dynamics of phytoplankton-zooplankton systems with toxin producing phytoplankton. Applied Mathematics and Computation, 227, (2014), pp.717-740.
     
  
• J. Baglama and D. Richmond, Implicitly restarting the LSQR algorithm. Electronic Transactions on Numerical Analysis, 42, (2014), pp.85-105. pdf file
  
  • Click here to download the MATLAB code irlsqr.m
  
  
• J. Baglama, C. Fenu, L. Reichel, and G. Rodriguez, Analysis of directed networks via partial singular value decomposition and Gauss quadrature. Linear Algebra and its Applications, 456, (2014), pp.93-121.
     
  
• M. Jandron, A. Ruffa, and J. Baglama, An asynchronous direct solver for banded linear systems. Numerical Algorithms, 76, (2017), pp.211-235.
  • Click here to download the README file README.txt.
  • Click here to download the MATLAB code Algorithm_21_TMFS_Driver.m.
  • Click here to download the MATLAB code Algorithm_21_TMFS.m.
  • Click here to download the MATLAB code Algorithm_22_TMFBS_Driver.m.
  • Click here to download the MATLAB code Algorithm_22_TMFBS.m.
  • Click here to download the MATLAB code Algorithm_31_BMFS.m.
  • Click here to download the MATLAB code Algorithm_31_BMFS_Driver.m.
  • Click here to download the FORTRAN 95 code Algorithm_31_BMFS.f90
  
  
• J. Baglama, T. Bella, and J. Picucci, Hybrid iterative refined method for computing a few extreme eigenpairs of a symmetric matrix. SIAM Journal on Scientific Computing, 43(5), (2021), pp.S200-S224. pdf file
  
  • Click here to download the MATLAB code trreigs.m
  
  
• J. Baglama, V. Perovic, and J. Picucci, Hybrid iterative refined restarted Lanczos bidiagonalization methods. Numerical Algorithms, Vol. 92, (2023), pp. 1183-1212. pdf file
  • Click here to download the readme file readme.txt.
  • Click here to download the MATLAB driver code driver_trrsvds_rd2svds.m.
  • Click here to download the MATLAB code trrsvds.m.
  • Click here to download the MATLAB code rd2svds.m
  
  
• J. Baglama and V. Perovic, Explicit deflation in Golub-Kahan-Lanczos bidiagonalization methods. Electronic Transactions on Numerical Analysis, 58, (2023), pp. 164-176.
     
  
• J. Baglama, V. Perovic, and T. Toolan, Note on a Rank-one Modification of the Singular Value Decomposition Applied Mathematics and Computation, Vol. 457, 15 Nov. (2023).
     
  
• J. Baglama, J. Chavez-Casillas and V. Perovic, A Hybrid Algorithm for Com- puting a Partial Singular Value Decomposition Satisfying a Given Threshold Numerical Algorithms, accepted on July 25, 2024 and published on August 6, 2024. DOI 10.1007/s11075-024-01906-9
  • Click here for the GitHub site for the codes.

Book Chapters

1. S. J. Jang and J. Baglama, Nutrient-Plankton Interaction with a Toxin in a Variable Input Nutrient Environment Current Development in Mathematical Biology, Proceedings of the Conference on Mathematical Biology and Dynamical Systems, Series on Knots and Everything, (2007), ISBN 981-270-015-3.
      
   
2. J. Baglama, Creating a First Online Course in the Mathematics Department Chapter in the Book: Taking your class online: A multidisciplinary journey, Information Age Publishing, (2012), ISBN:161-735-593-3.
      
   
3. J. Baglama, IRLBA: Fast Partial Singular Value Decomposition Method Chapter in the Book: Handbook of Big Data, Taylor and Francis, (2016), ISBN: 148-224-907-3.