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1

Currie, James D. The complexity of the simplex algorithm. [Ottawa]: Carleton University, Mathematics and Statistics, 1985.

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2

Sima, Vasile. Algorithms for linear-quadratic optimization. New York: M. Dekker, 1996.

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3

Mathematical algorithms for linear regression. Boston: Academic Press, 1991.

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4

Kontoghiorghes, Erricos John. Parallel Algorithms for Linear Models. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4571-2.

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5

Lee, P. Synthesizing linear-array algorithms from nested for loop algorithms. New York: Courant Institute of Mathematical Sciences, New York University, 1988.

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6

I, Agoshkov V., and Shuti͡a︡ev V. P, eds. Sopri͡a︡zhennye uravnenii͡a︡ i algoritmy vozmushcheniĭ. Moskva: Akademii͡a︡ nauk SSSR, Otdel vychislitelʹnoĭ matematiki, 1986.

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7

Panik, Michael J. Linear programming: Mathematics, theory and algorithms. Dordrecht: Kluwer Academic, 1996.

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8

Abdullah, Jalaluddin. Fixed point algorithms for linear programming. Birmingham: University of Birmingham, 1992.

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9

service), SpringerLink (Online, ed. Max-linear Systems: Theory and Algorithms. London: Springer-Verlag London Limited, 2010.

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10

Linear network optimization: Algorithms and codes. Cambridge, Mass: MIT Press, 1991.

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11

Panik, Michael J., ed. Linear Programming: Mathematics, Theory and Algorithms. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-3434-7.

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12

Butkovič, Peter. Max-linear Systems: Theory and Algorithms. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-299-5.

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13

Mishra, B. A linear time algorithm for finding an ambitus. New York: Courant Institute of Mathematical Sciences, New York University, 1989.

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14

Fang, Shu-Cherng. Linear optimization and extensions: Theory and algorithms. Englewood Cliffs, N.J: Prentice Hall, 1993.

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15

The linear ordering problem: Algorithms and applications. Berlin: Heldermann, 1985.

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16

Spedicato, Emilio, ed. Computer Algorithms for Solving Linear Algebraic Equations. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76717-3.

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17

Nicol, David M. A multistage linear array assignment problem. Hampton, Va: ICASE, 1988.

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18

Potential function methods for approximately solving linear programming problems: Theory and practice. Boston: Kluwer Academic Publishers, 2002.

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19

Lobo, M. Stability theorems concerning high order explicit algorithms for the linear advection equation. Cranfield, Eng: College of Aeronautics, Cranfield Institute of Technology, 1985.

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20

Ye, Yinyu. Interior point algorithms: Theory and analysis. New York: Wiley, 1997.

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21

Arbel, Ami. Exploring interior-point linear programming: Algorithms and software. Cambridge, Mass: MIT Press, 1993.

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22

Engquist, Bjorn. Fast wavelet based algorithms for linear evolution equations. Hampton, Va: Institute for Computer Applications in Science and Engineering, 1992.

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23

Eiguren, Jakoba. Soft-decision decoding algorithms for linear block codes. Manchester: University of Manchester, 1994.

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24

Kepner, Jeremy V. Graph algorithms in the language of linear algebra. Philadelphia: Society for Industrial and Applied Mathematics, 2011.

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25

Goedecker, S. An efficient linear scaling algorithm for tight binding molecular dynamics. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1994.

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26

Biberdorf, Ė. A. Garantirovannai︠a︡ tochnostʹ sovremennykh algoritmov lineĭnoĭ algebry. Novosibirsk: Izd-vo SO RAN, 2006.

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27

I, Agoshkov V., and Shuti͡a︡ev V. P, eds. Sopri͡a︡zhennye uravnenii͡a︡ i algoritmy vozmushcheniĭ v nelineĭnykh zadachakh matematicheskoĭ fiziki. Moskva: Akademii͡a︡ nauk SSSR, Otdel vychislitelʹnoĭ matematiki, 1991.

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28

Crama, Yves. Boolean functions: Theory, algorithms, and applications. Cambridge: Cambridge University Press, 2011.

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29

Morton, David P. An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling. Monterey, Calif: Naval Postgraduate School, 1994.

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30

Computational techniques of the simplex method. Boston: Kluwer Academic Publishers, 2003.

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31

Algorithms for computer-aided design of linear microwave circuits. Boston: Artech House, 1990.

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32

Murthy, C. Siva Ram. New parallel algorithms for direct solution of linear equations. New York: J. Wiley, 2000.

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33

Golub, Gene H., and Paul Van Dooren, eds. Numerical Linear Algebra, Digital Signal Processing and Parallel Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-75536-1.

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34

NATO, Advanced Study Institute on Linear Algebra Digital Signal Processing and Parallel Algorithms (1988 Leuven Belgium). Numerical linear algebra, digital signal processing, and parallel algorithms. Berlin: Springer-Verlag, 1991.

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35

Canada. Defence Research Establishment Atlantic. Least Squares Algorithm For Fitting Piecewise Linear Functions on Fixed Domains. S.l: s.n, 1985.

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36

Blair, Charles. The iterative step in the linear programming algorithm of N. Karmarkar. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1985.

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37

I, Agoshkov V., and Shuti͡a︡ev V. P, eds. Adjoint equations and perturbation algorithms in nonlinear problems. Boca Raton, Fla: CRC Press, 1996.

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38

K, Godunov S., ed. Vvedenie v vychislitelʹnui͡u︡ lineĭnui͡u︡ algebru: S prilozheniem algoritmov na FORTRANe. Novosibirsk: Nauka, Sibirskoe otd-nie, 1991.

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39

Klerk, Etienne de. Aspects of semidefinite programming: Interior point algorithms and selected applications. New York: Springer, 2011.

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40

Mersini, Nexhat. Sparse matrices of high order: Algorythms for reordering, inversion and solution of systems of linear equations = Matrica te rralla te rendeve te larta : algoritme per aranzhimin, inversimin dhe zgjidhjen e sistemeve te ekuacioneve lineare. Tirane: VEFA, 1996.

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41

Kreiner, Svend. Collapsibility of multidimensional contingency tables: Theorems, algorithms, and programs. [Copenhagen, Denmark]: Danish Institute for Educational Research, 1987.

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42

Van Landingham, Hugh F., 1935-, ed. Algorithms for computer-aided design of multivariable control systems. New York: M. Dekker, 1993.

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43

Koenker, Roger W. An interior point algorithm for nonlinear quantile regression / Roger Koenker ; Beum J. Park. Champaign: University of Illinois at Urbana-Champaign, 1992.

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44

Smith, Fraser B. Mixed model analyses of censored normal distributions via the EM algorithm. Chapel Hill, N.C: Dept. of Biostatistics, Univ. of North Carolina at Chapel Hill, 1992.

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45

Tamás, Terlaky, Vial J. P, and Roos Cornelis 1941-, eds. Interior point methods for linear optimization. 2nd ed. New York: Springer, 2006.

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46

Bultheel, Adhemar. Linear algebra, rational approximation, and orthogonal polynomials. Amsterdam: Elsevier, 1997.

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47

Povitsky, Alex. Wavefront cache-friendly algorithm for compact numerical schemes. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1999.

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48

Graham, Ronald E. Linearization of digital derived rate algorithm for use in linear stability analysis. [Washington, DC?]: National Aeronautics and Space Administration, 1985.

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49

Edelsbrunner, Herbert. The upper envelope of piecewise linear functions: algorithms and applications. New York: Courant Institute of Mathematical Sciences, New York University, 1987.

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50

Bisseling, Rob H. Parallel Scientific Computation. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198788348.001.0001.

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This book explains how to use the bulk synchronous parallel (BSP) model to design and implement parallel algorithms in the areas of scientific computing and big data. Furthermore, it presents a hybrid BSP approach towards new hardware developments such as hierarchical architectures with both shared and distributed memory. The book provides a full treatment of core problems in scientific computing and big data, starting from a high-level problem description, via a sequential solution algorithm to a parallel solution algorithm and an actual parallel program written in the communication library BSPlib. Numerical experiments are presented for parallel programs on modern parallel computers ranging from desktop computers to massively parallel supercomputers. The introductory chapter of the book gives a complete overview of BSPlib, so that the reader already at an early stage is able to write his/her own parallel programs. Furthermore, it treats BSP benchmarking and parallel sorting by regular sampling. The next three chapters treat basic numerical linear algebra problems such as linear system solving by LU decomposition, sparse matrix-vector multiplication (SpMV), and the fast Fourier transform (FFT). The final chapter explores parallel algorithms for big data problems such as graph matching. The book is accompanied by a software package BSPedupack, freely available online from the author’s homepage, which contains all programs of the book and a set of test programs.
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