Linearization of Second-Order Non-Linear Ordinary Differential Equations: A Geometric Approach

Abstract

Using the coefficients of a system semilinear cubic in the first derivative second order differential equations one defines a connection in the space of the independent and dependent variables, which is specified modulo two free parameters. In this way, to any such equation one associates an affine space which is not necessarily Riemannian, that is, a metric is not required. If such a metric exists, then under the Cartan parametrization the geodesic equations of the metric coincide with the system of the considered semilinear equations. In the present work, we consider semilinear cubic in the first derivative second order differential equations whose Lie symmetry algebra is the sl(3,R). The covariant condition for these equations is the vanishing of the curvature tensor. We demonstrate the method in the solution of the Painlevé-Ince equation and in a system of two equations. Because the approach is geometric, the number of equations in the system is not important besides the complication in the calculations. It is shown that it is possible to linearize an equation in this form using a different covariant condition, for example, assuming the space to be of constant non-vanishing curvature. Finally, it is shown that one computes the associated metric to a semilinear cubic in the first derivatives differential equation using the inverse transformation derived from the transformation of the connection.

Solving Nonlinear Second-Order ODEs via the Eisenhart Lift and Linearization

“…For instance, the oscillator y ′′ + y = 0 becomes d 2 Y dX 2 = 0 after the change of variables X = tan x and Y = y cos x . Because the linearization property leads to a simple approach to the construction of a solution, it has been the subject of interest in a series of studies; see, for instance, [38][39][40][41][42][43][44][45] and the references therein.…”

“…We know that if the n − 1 equations admit n(n + 2) symmetries, then they can be linearized. Thus, an equivalent geometric linearization criterion is the structure function of (40) to define a maximally symmetric space; see the discussion in [42].…”