Editing Computational physics (section)

== Methods and algorithms ==
Because computational physics uses a broad class of problems, it is generally divided amongst the different mathematical problems it numerically solves, or the methods it applies. Between them, one can consider:

* [[Root-finding_algorithm|root finding]] (using e.g. [[Newton's_method|Newton-Raphson method]])
* [[system of linear equations]] (using e.g. [[LU decomposition]])
* [[ordinary differential equation]]s (using e.g. [[Runge–Kutta methods]])
* [[integral|integration]] (using e.g. [[Romberg's_method|Romberg method]] and [[Monte Carlo integration]])
* [[partial differential equation]]s (using e.g. [[finite difference]] method and [[Relaxation_(iterative_method)|relaxation]] method)
* [[matrix eigenvalue problem]] (using e.g. [[Jacobi eigenvalue algorithm]] and [[power iteration]])

All these methods (and several others) are used to calculate physical properties of the modeled systems.

Computational physics also borrows a number of ideas from [[computational chemistry]] - for example, the [[density functional theory]] used by computational solid state physicists to calculate properties of solids is basically the same as that used by chemists to calculate the properties of molecules.

Furthermore, computational physics encompasses the [[performance tuning|tuning]] of the [[self-tuning#Examples|software]]/[[:Category:Computer hardware tuning|hardware structure]] to solve the problems (as the problems usually can be very large, in [[high performance computing|processing power need]] or in [[high-throughput computing|memory requests]]).