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Published online by Cambridge University Press:  30 August 2023

Alex Gezerlis
Alex Gezerlis
Affiliation:
University of Guelph, Ontario
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Numerical Methods in Physics with Python , pp. 671 - 676
Publisher: Cambridge University Press
Print publication year: 2023

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References

Abramowitz, M., and Stegun, I. A. (eds). 1965. Handbook of Mathematical Functions. Dover.Google Scholar
Acton, F. S. 1990. Numerical Methods That Work. Mathematical Association of America.CrossRefGoogle Scholar
Acton, F. S. 1996. Real Computing Made Real. Princeton University Press.Google Scholar
Allen, M. B. III, and Isaacson, E. L. 2019. Numerical Analysis for Applied Science. Second edn. John Wiley & Sons.CrossRefGoogle Scholar
Allen, M. P, and Tildesley, D. J. 2019. Computer Simulation of Liquids. Second edn. Oxford University Press.Google Scholar
Andrae, R., Schulze-Hartung, T., and Melchior, P. 2010. Dos and don’ts of reduced chi-squared. arXiv, 1012.3754.Google Scholar
Antoniou, A., and Lu, W.-S. 2007. Practical Optimization. Springer.Google Scholar
Arfken, G. B., and Weber, H. J. 2005. Mathematical Methods for Physicists. Sixth edn. Elsevier.Google Scholar
Ascher, U. M., and Greif, C. 2011. A First Course in Numerical Methods. Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Audi, G., Wapstra, A. H., and Thibault, C. 2003. The Ame2003 atomic mass evaluation. Nucl Phys A, 729, 337.CrossRefGoogle Scholar
Bailey, D. H., and Swarztrauber, P. N. 1994. A fast method for the numerical evaluation of continuous Fourier and Laplace transforms. SISC, 15, 1105.CrossRefGoogle Scholar
Bajorski, P. 2012. Statistics for Imaging, Optics, and Photonics. John Wiley & Sons.Google Scholar
Barlow, R. J. 1989. Statistics. John Wiley & Sons.Google Scholar
Baym, G. 1969. Lectures on Quantum Mechanics. Westview Press.Google Scholar
Becca, F., and Sorella, S. 2017. Quantum Monte Carlo Approaches for Correlated Systems. Cambridge University Press.CrossRefGoogle Scholar
Bender, C. A., and Orszag, S. A. 1978. Advanced Mathematical Methods for Scientists and Engineers. McGraw-Hill.Google Scholar
Bernardo, J. M., and Smith, A. F. M. 2000. Bayesian Theory. John Wiley & Sons.Google Scholar
Berrut, J.-P., and Trefethen, L. N. 2004. Barycentric Lagrange interpolation. SIAM Review, 46, 501.CrossRefGoogle Scholar
Beu, T. A. 2015. Introduction to Numerical Programming. CRC Press.Google Scholar
Bevington, P. R., and Robinson, D. K. 2003. Data Reduction and Error Analysis for the Physical Sciences. Third edn. McGraw-Hill.Google Scholar
Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Springer.Google Scholar
Blatt, J. M., and Weisskopf, V. F. 1979. Theoretical Nuclear Physics. Springer.CrossRefGoogle Scholar
Boudreau, J. F., and Swanson, E. S. 2018. Applied Computational Physics. Oxford University Press.Google Scholar
Box, G. E. P., and Luceño, A. 1997. Statistical Control. John Wiley & Sons.Google Scholar
Brent, R. P. 1973. Algorithms for Minimization without Derivatives. Prentice-Hall.Google Scholar
Broyden, C. G. 1965. A class of methods for solving nonlinear simultaneous equations. Math Comp, 19, 577.CrossRefGoogle Scholar
Burden, R. L., Faires, J. D., and Burden, A. M. 2016. Numerical Analysis. Tenth edn. Cengage Learning.Google Scholar
Byron, F. W. Jr, and Fuller, R. W. 1992. Mathematics of Classical and Quantum Physics. Dover.Google Scholar
Casella, G., and Berger, R. L. 2002. Statistical Inference. Second edn. Duxbury.Google Scholar
Ceder, N. 2018. The Quick Python Book. Third edn. Manning Publications.Google Scholar
Conte, S. D., and de Boor, C. 1980. Elementary Numerical Analysis. Third edn. McGraw-Hill.Google Scholar
Creutz, M. 1983. Quarks, Gluons and Lattices. Cambridge University Press.Google Scholar
Cuzzocrea, A. et al. 2020. Variational principles in quantum Monte Carlo. J Chem Theory Comput, 16, 4203.CrossRefGoogle ScholarPubMed
Daley, A. J. 2014. Quantum trajectories and open many-body quantum systems. Adv Phys, 63, 77.CrossRefGoogle Scholar
Dalhquist, G., and Björck, Å. 1974. Numerical Methods. Prentice-Hall.Google Scholar
Davio, M. 1981. Kronecker products and shuffle algebra. IEEE Trans Comp, C- 30, 116.CrossRefGoogle Scholar
Davis, P. J., and Rabinowitz, P. 1984. Methods of Numerical Integration. Second edn. Academic Press.Google Scholar
Deisenroth, M. P., Faisal, A. A., and Ong, C. S. 2020. Mathematics for Machine Learning. Cambridge University Press.CrossRefGoogle Scholar
Delves, L. M., and Mohamed, J. L. 1985. Computational Methods for Integral Equations. Cambridge University Press.CrossRefGoogle Scholar
Demmel, J. W. 1997. Applied Numerical Linear Algebra. Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Downey, A. B. 2016. Think Python. Second edn. O’Reilly.Google Scholar
Duncan, A. 2012. The Conceptual Foundations of Quantum Field Theory. Oxford University Press.Google Scholar
Dunn, W. L., and Shultis, J. K. 2012. Exploring Monte Carlo Methods. Elsevier.Google Scholar
Efron, B., and Tibshirani, R. J. 1993. An Introduction to the Bootstrap. Springer.CrossRefGoogle Scholar
Fetter, A. L., and Walecka, J. D. 1980. Theoretical Mechanics of Particles and Continua. McGraw-Hill.Google Scholar
Feynman, R. P., Leighton, R. B., and Sands, M. L. 2010. The Feynman Lectures on Physics, Vol. 3. New millennium edn. Addison-Wesley.Google Scholar
Fletcher, R. 1987. Practical Methods of Optimization. Second edn. John Wiley & Sons.Google Scholar
Flügge, S. 1994. Practical Quantum Mechanics. Springer.Google Scholar
Franklin, J. 2013. ComputationalMethods for Physics. Cambridge University Press.Google Scholar
Freeman, W. T., and Adelson, E. H. 1991. The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell, 13, 891.CrossRefGoogle Scholar
Gander, W., Gander, M. J., and Kwok, F. 2014. Scientific Computing. Springer.Google Scholar
Gil, A., Segura, J., and Temme, N. M. 2007. Numerical Methods for Special Functions. Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (eds). 1996. Markov Chain Monte Carlo in Practice. CRC Press.Google Scholar
Giorgini, S., Pitaevskii, L. P., and Stringari, S. 2008. Theory of ultracold atomic Fermi gases. Rev Mod Phys, 80, 1215.CrossRefGoogle Scholar
Goldberg, D. 1991. What every computer scientist should know about floating-point arithmetic. ACM Comp Surv, 23, 5.CrossRefGoogle Scholar
Goldenfeld, N. 2019. Lectures on Phase Transitions and the Renormalization Group. CRC Press.Google Scholar
Goldstein, H., Poole, C., and Safko, J. 2001. Classical Mechanics. Third edn. Addison-Wesley.Google Scholar
Golub, G. H., and Van Loan, C. F. 1996. Matrix Computations. Third edn. Johns Hopkins University Press.Google Scholar
Goodfellow, I., Bengio, Y., and Courville, A. 2016. Deep Learning. MIT Press.Google Scholar
Greenbaum, A., and Chartier, T. P. 2012. Numerical Methods. Princeton University Press.Google Scholar
Greene, W. H. 2018. Econometric Analysis. Eighth edn. Pearson.Google Scholar
Griffiths, D. J. 2008. Introduction to Elementary Particles. Second edn. Wiley-VCH.Google Scholar
Griffiths, D. J. 2017. Introduction to Electrodynamics. Fourth edn. Cambridge University Press.CrossRefGoogle Scholar
Hammarling, S. 2005. An introduction to the quality of computed solutions. Pages 43–76 of: Einarsson, B. (ed), Accuracy and Reliability in Scientific Computing. Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Hamming, R. W. 1973. Numerical Methods for Scientists and Engineers. Second edn. McGraw-Hill.Google Scholar
Hamming, R. W. 2012. Introduction to Applied Numerical Analysis. Dover.Google Scholar
Heath, M. T. 2002. Scientific Computing. Second edn. McGraw-Hill.Google Scholar
Higham, N. J. 2002. Accuracy and Stability of Numerical Algorithms. Second edn. Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Hildebrand, F. D. 1974. Introduction to Numerical Analysis. Second edn. McGraw-Hill.Google Scholar
Hilditch, R. W. 2001. An Introduction to Close Binary Stars. Cambridge University Press.CrossRefGoogle Scholar
Hill, C. 2020. Learning Scientific Programming with Python. Second edn. Cambridge University Press.CrossRefGoogle Scholar
Hoffman, J. D. 2001. Numerical Methods for Engineers and Scientists. Second edn. Marcel Dekker.Google Scholar
Isaacson, E., and Keller, H. B. 1994. Analysis of Numerical Methods. Dover.Google Scholar
Iserles, A. 2008. A First Course in the Numerical Analysis of Differential Equations. Second edn. Cambridge University Press.CrossRefGoogle Scholar
Izaac, J., and Wang, J. 2018. Computational Quantum Mechanics. Springer.CrossRefGoogle Scholar
Jackson, J. D. 1999. Classical Electrodynamics. Third edn. John Wiley & Sons.Google Scholar
Jammer, M. 1966. The Conceptual Development of Quantum Mechanics. McGraw-Hill.Google Scholar
Jaynes, E. T. 2003. Probability Theory. Cambridge University Press.CrossRefGoogle Scholar
Kahan, W. 1966. Numerical linear algebra. Can Math Bul, 7, 757.CrossRefGoogle Scholar
Kahan, W. 1981. Why do we need a floating-point arithmetic standard? Technical Report, UC Berkeley.Google Scholar
Kahan, W. 2005. How futile are mindless assessments of roundoff in floating-point computation? Hous Sympos XVI.Google Scholar
Kalos, M. H., and Whitlock, P. A. 2008. Monte Carlo Methods. Second edn. Wiley-VCH.CrossRefGoogle Scholar
Kendall, M. G., and Stuart, A. 1961. The Advanced Theory of Statistics, Vol. 2. Hafner Publishing Company.Google Scholar
Kernighan, B. W., and Pike, R. 1999. The Practice of Programming. Addison-Wesley.Google Scholar
Kernighan, B. W., and Plauger, P. J. 1976. Software Tools. Addison-Wesley.CrossRefGoogle Scholar
Kittel, C. 2005. Introduction to Solid State Physics. Eighth edn. John Wiley & Sons.Google Scholar
Kiusalaas, J. 2013. Numerical Methods in Engineering with Python 3. Cambridge University Press.CrossRefGoogle Scholar
Klainerman, S. 2008. Partial differential equations. Pages 455–483 of: Gowers, T. (ed), The Princeton Companion to Mathematics. Princeton University Press.Google Scholar
Knuth, D. E. 1998. The Art of Computer Programming, Vol. 2. Third edn. Addison-Wesley.Google Scholar
Koonin, S. E., and Meredith, D. C. 1990. Computational Physics. Addison-Wesley.Google Scholar
Krylov, V. I. 2005. Approximate Calculation of Integrals. Dover.Google Scholar
Lacava, F. 2016. Classical Electrodynamics. Springer.CrossRefGoogle Scholar
Landau, R., and Páez, M. 2018. Computational Problems for Physics. CRC Press.CrossRefGoogle Scholar
Langer, J. S., and Vosko, S. H. 1960. The shielding of a fixed charge in a high-density electron gas. J Phys Chem Sol, 12, 196.CrossRefGoogle Scholar
Le Cam, L. 1990. Maximum likelihood: an introduction. IS Review, 58, 153.Google Scholar
Liboff, R. L. 2002. Introductory Quantum Mechanics. Fourth edn. Pearson.Google Scholar
Longair, M. 2013. Quantum Concepts in Physics. Cambridge University Press.CrossRefGoogle Scholar
Lovitch, L., and Rosati, S. 1965. Direct numerical integration of the two-nucleon Schrödinger equation with tensor forces. Phys Rev, 140, B877.CrossRefGoogle Scholar
Lummer, O., and Pringsheim, E. 1897. Die Strahlung eines “schwarzen” Körpers zwischen 100 and 1300 C. Ann Phys Chem, 299, 395.CrossRefGoogle Scholar
Lyness, J. N., and Moller, C. B. 1967. Numerical differentiation of analytic functions. SIAM J Numer Anal, 4, 202.CrossRefGoogle Scholar
Lyons, L. 2013. Bayes and frequentism: a particle physicist’s perspective. Contemp Phys, 54, 1.CrossRefGoogle Scholar
Mariño, M. 2015. Instantons and Large N. Cambridge University Press.CrossRefGoogle Scholar
McConnell, S. 2004. Code Complete. Second edn. Microsoft Press.Google Scholar
McKinney, W. 2022. Python for Data Analysis. Third edn. O’Reilly.Google Scholar
Merzbacher, M. 1970. Quantum Mechanics. Second edn. John Wiley & Sons.Google Scholar
Meyn, S., and Tweedie, R. L. 2009. Markov Chains and Stochastic Stability. Second edn. Cambridge University Press.CrossRefGoogle Scholar
Millikan, R. A. 1916. A Direct photoelectric determination of Planck’s “h”. Phys Rev, 7, 355.CrossRefGoogle Scholar
Newman, M. 2012. Computational Physics. Revised edn. CreateSpace.Google Scholar
Nocedal, J., and Wright, S. J. 2006. Numerical Optimization. Second edn. Springer.Google Scholar
Oliveira, S., and Stewart, D. 2006. Writing Scientific Software. Cambridge University Press.CrossRefGoogle Scholar
Overton, M. L. 2001. Numerical Computing with IEEE Floating Point Arithmetic. Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Pang, T. 2006. An Introduction to Computational Physics. Second edn. Cambridge University Press.CrossRefGoogle Scholar
Pavelich, R. L., and Marsiglio, F. 2015. The Kronig-Penney model extended to arbitrary potentials via numerical matrix mechanics. Am J Phys, 83, 773.CrossRefGoogle Scholar
Peskin, M. E., and Schroeder, D. V. 2018. An Introduction to Quantum Field Theory. CRC Press.CrossRefGoogle Scholar
Pethick, C. J., and Smith, H. 2008. Bose–Einstein Condensation in Dilute Gases. Second edn. Cambridge University Press.CrossRefGoogle Scholar
Pines, D., and Nozières, P. 1989. The Theory of Quantum Liquids, Vol. 1. Westview Press.Google Scholar
Poisson, E., and Will, C. M. 2014. Gravity. Cambridge University Press.CrossRefGoogle Scholar
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. 1992. Numerical Recipes in Fortran. Second edn. Cambridge University Press.Google Scholar
Ralston, A., and Rabinowitz, P. 1978. A First Course in Numerical Analysis. Second edn. McGraw-Hill.Google Scholar
Ramalho, L. 2022. Fluent Python. Second edn. O’Reilly.Google Scholar
Ree, F. H., and Holt, A. C. 1973. Thermodynamic properties of the alkali-halide crystals. Phys Rev B, 8, 826.CrossRefGoogle Scholar
Richardson, O. W., and Compton, K. T. 1912. The photoelectric effect. Phil Mag, 24, 575.CrossRefGoogle Scholar
Ridders, C. 1979. A new algorithm for computing a single root of a real continuous function. IEEE Trans Circ Syst, 26, 979.CrossRefGoogle Scholar
Robert, C. P., and Casella, G. 2004. Monte Carlo Statistical Methods. Second edn. Springer.CrossRefGoogle Scholar
Roberts, G. O., Gelman, A., and Gilks, W. R. 1997. Weak convergence and optimal scaling of random walk Metropolis algorithms. Ann Appl Prob, 7, 110.Google Scholar
Rogers, S., and Girolami, M. 2017. A First Course in Machine Learning. Second edn. CRC Press.Google Scholar
Schutz, B. F. 2022. A First Course in General Relativity. Third edn. Cambridge University Press.CrossRefGoogle Scholar
Scopatz, A., and Huff, K. D. 2015. Effective Computation in Physics. O’Reilly.Google Scholar
Shankar, R. 1994. Principles of Quantum Mechanics. Second edn. Plenum Press.CrossRefGoogle Scholar
Shapiro, S. L., and Teukolsky, S. A. 2004. Black Holes, White Dwarfs, and Neutron Stars. Wiley-VCH.Google Scholar
Širca, S., and Horvat, M. 2018. Computational Methods in Physics. Second edn. Springer.CrossRefGoogle Scholar
Slatkin, B. 2019. Effective Python. Second edn. Addison-Wesley.Google Scholar
Stewart, G. W. 1973. Introduction to Matrix Computations. Academic Press.Google Scholar
Stewart, G. W., and Sun, J. 1990. Matrix Perturbation Theory. Academic Press.Google Scholar
Stoer, J., and Bulirsch, R. 1993. Introduction to Numerical Analysis. Second edn. Springer.CrossRefGoogle Scholar
Stoks, V. G. J. et al. 1993. Partial-wave analysis of all nucleon-nucleon scattering data below 350 MeV. Phys Rev C, 48, 792.CrossRefGoogle ScholarPubMed
Stowe, K. 2007. An Introduction to Thermodynamics and Statistical Mechanics. Second edn. Cambridge University Press.CrossRefGoogle Scholar
Strang, G. 2005. Linear Algebra and Its Applications. Fourth edn. Brooks/Cole.Google Scholar
Szebehely, V. 1967. Theory of Orbits. Academic Press.Google Scholar
Szegö, G. 1975. Orthogonal Polynomials. Fourth edn. American Mathematical Society.Google Scholar
Taylor, J. R. 1972. Scattering Theory. John Wiley & Sons.Google Scholar
Theodoridis, S. 2020. Machine Learning. Second edn. Academic Press.Google Scholar
Thijssen, J. M. 2007. Computational Physics. Second edn. Cambridge University Press.CrossRefGoogle Scholar
Thornton, S. T., and Marion, J. B. 2004. Classical Dynamics of Particles and Systems. Fifth edn. Brooks/Cole.Google Scholar
Tierney, L. 1994. Markov chains for exploring posterior distributions. Ann Stat, 22, 1701.Google Scholar
Toussaint, D. 1989. Introduction to algorithms for Monte Carlo simulations and their application to QCD. Comput Phys Commun, 56, 69.CrossRefGoogle Scholar
Trefethen, L. N. 2019. Approximation Theory and Approximation Practice. Extended edn. Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Trefethen, L. N., and Bau, D. III. 1997. Numerical Linear Algebra. Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Tucker, W. 2011. Validated Numerics. Princeton University Press.Google Scholar
van der Vaart, A. W. 1998. Asymptotic Statistics. Cambridge University Press.CrossRefGoogle Scholar
Wasserman, L. 2004. All of Statistics. Springer.CrossRefGoogle Scholar
Weinberg, S. 2020. Lectures on Astrophysics. Cambridge University Press.Google Scholar
Wilkinson, J. H. 1963. Rounding Errors in Algebraic Processes. Prentice-Hall.Google Scholar
Wilkinson, J. H. 1965. The Algebraic Eigenvalue Problem. Oxford University Press.Google Scholar
Williams, B. 1978. Descartes: The Project of Pure Inquiry. Penguin.Google Scholar
Zinn-Justin, J. 2021. Quantum Field Theory and Critical Phenomena. Fifth edn. Oxford University Press.CrossRefGoogle Scholar

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  • Bibliography
  • Alex Gezerlis, University of Guelph, Ontario
  • Book: Numerical Methods in Physics with Python
  • Online publication: 30 August 2023
  • Chapter DOI: https://doi.org/10.1017/9781009303897.013
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  • Bibliography
  • Alex Gezerlis, University of Guelph, Ontario
  • Book: Numerical Methods in Physics with Python
  • Online publication: 30 August 2023
  • Chapter DOI: https://doi.org/10.1017/9781009303897.013
Available formats
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  • Bibliography
  • Alex Gezerlis, University of Guelph, Ontario
  • Book: Numerical Methods in Physics with Python
  • Online publication: 30 August 2023
  • Chapter DOI: https://doi.org/10.1017/9781009303897.013
Available formats
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