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When physical phenomena are modeled with non-linear equations, they are generally approximated by linear differential equations for an easier solution. The few non-linear ODEs that can be solved explicitly are generally solved by transforming the equation into an equivalent linear ODE (see, for example Riccati equation). [5]
Numerical methods for solving first-order IVPs often fall into one of two large categories: [5] linear multistep methods, or Runge–Kutta methods.A further division can be realized by dividing methods into those that are explicit and those that are implicit.
The study of these differential equations with constant coefficients dates back to Leonhard Euler, who introduced the exponential function e x, which is the unique solution of the equation f′ = f such that f(0) = 1. It follows that the n th derivative of e cx is c n e cx, and this allows solving homogeneous linear differential equations ...
For an arbitrary system of ODEs, a set of solutions (), …, are said to be linearly-independent if: + … + = is satisfied only for = … = =.A second-order differential equation ¨ = (,, ˙) may be converted into a system of first order linear differential equations by defining = ˙, which gives us the first-order system:
For example, consider the ordinary differential equation ′ = + The Euler method for solving this equation uses the finite difference quotient (+) ′ to approximate the differential equation by first substituting it for u'(x) then applying a little algebra (multiplying both sides by h, and then adding u(x) to both sides) to get (+) + (() +).
Most numerical solvers require ordinary differential equations and algebraic equations of the form = (,,), = (,,). It is a non-trivial task to convert arbitrary DAE systems into ODEs for solution by pure ODE solvers.
Linear multistep methods are used for the numerical solution of ordinary differential equations. Conceptually, a numerical method starts from an initial point and then takes a short step forward in time to find the next solution point. The process continues with subsequent steps to map out the solution.
Relaxation methods were developed for solving large sparse linear systems, which arose as finite-difference discretizations of differential equations. [2] [3] They are also used for the solution of linear equations for linear least-squares problems [4] and also for systems of linear inequalities, such as those arising in linear programming.