Numeric interpolation: Spline and Lagrange interpolation.
Numeric differentiation: rules of 2, 3 and 5 points and Richardson method. Numeric integration: Trapezoid Rule, Simpson’s rule, Romberg and Gaussian Quadrature.
Zeros and extrema of the function of a variable: bisection, secant, regula falsi and the newton-raphson methods.
Linear systems of equations: Gauss-Jordan elimination, LU factorization, Cholesky factorization and QR factorization.
Extrema of the function of various variables: maximum decrease method and conjugated gradients method, genetic algorithms, simulated annealing and search methods in a pattern.
Applications in Physics (molecular geometry, etc.).
Monte Carlo method: integration, radioactive decay, diffusion. Random walks and Metropolis algorithm. Ising Model.
Problems of intrinsic values: diagnolization of Schrödinger equation.
Differential equations: Euler’s method, Runge-Kutta methods and predictor-corrector methods.
The cushioned and forced pendulum. Chaos.
Resolution of the Schrödinger equation by means of the integration of the differential equation: the Numerov’s method.
Laplace and Poisson’s equations.
Quantum Monte Carlo methods: the hydrogen and helium atoms and H2 and H2+ Molecules.
PRESS, William [et.al.]. Numerical Recipes in F77/F90/C/C++: The Art of Scientific Computing, Cambridge: Cambridge University Press.
HJORTH-JENSEN, M. Computational Physics. http://www.uio.no/studier/emner/matnat/fys/FYS3150/h11/undervisningsmateriale/Lecture%20Notes/lectures2011.pdf
PANG, Tao (2006). An Introduction to Computational Physics. Cambridge: Cambridge University Press. ISBN: 978-0521825696.
ALLEN, M. P. and TILDESLEY, D. J. (1989). Computer Simulation of Liquids. Oxford: Clarendon Press. ISBN: 978-0198556459.
FRENKEL, Daan and SMIT, Berend (2001). Understanding Molecular Simulation: From Algorithms to Applications. New York: Academic Press. ISBN: 978-0122673511 .