Wronskian
In mathematics, the Wronskian (or Wrońskian) is a determinant introduced by and named by . It is used in the study of differential equations, where it can sometimes show linear independence in a set of solutions. Definition. The Wronskian of two differentiable functions and is . More generally, for real- or complex-valued functions , which are times differentiable on an interval , the Wronskian as a function on is defined by formula_1 That is, it is the determinant of the matrix constructed by placing the functions in the first row, the first derivative of each function in the second row, and so  on through the th derivative, thus forming a square matrix. When the functions are solutions of a linear differential equation, the Wronskian can be found explicitly using Abel's identity, even if the functions are not known explicitly. The Wronskian and linear independence. If the functions are linearly dependent, then so are the columns of the Wronskian (since differentiation is a linear operation), and the Wronskian vanishes. Thus, the Wronskian can be used to show that a set of differentiable functions is linearly independent on an interval by showing that it does not vanish identically. It may, however, vanish at  isolated points. A common misconception is that everywhere implies linear dependence, but pointed out that the functions and have continuous derivatives and their Wronskian vanishes everywhere, yet they are not linearly dependent in any neighborhood of . There are several extra conditions that ensure that the vanishing of the Wronskian in an interval implies linear dependence. Maxime Bôcher observed that if the functions are analytic, then the vanishing of the Wronskian in an interval implies that they are linearly dependent. gave several other conditions for the vanishing of the Wronskian to imply linear dependence; for example, if the Wronskian of functions  is identically zero and the Wronskians of of them do not all vanish at any point then the functions are linearly dependent. gave a more general condition that together with the vanishing of the Wronskian implies linear dependence. Over fields of positive characteristic the Wronskian may vanish even for linearly independent polynomials; for example, the Wronskian of and 1 is identically 0. Application to linear differential equations. In general, for an formula_2th order linear differential equation, if formula_3 solutions are known, the last one can be determined by using the Wronskian. Consider the second order differential equation in Lagrange's  notation formula_4 where formula_5, formula_6 are known. Let us call formula_7 the two solutions of the equation and form their Wronskian formula_8 Then differentiating formula_9 and using the fact that formula_10 obey the above differential equation shows that formula_11 Therefore, the Wronskian obeys a simple first order differential equation and can be exactly solved: formula_12 where formula_13 and formula_14 is a constant. Now suppose that we know one of the solutions, say formula_15. Then, by the definition of the Wronskian, formula_16 obeys a first order differential equation: formula_17 and can be solved exactly (at least in theory). The method is  easily generalized to higher order equations. Generalized Wronskians. For functions of several variables, a generalized Wronskian is a determinant of an by matrix with entries (with ), where each is some constant coefficient linear partial differential operator of order . If the functions are linearly dependent then all generalized Wronskians vanish. As in the single variable case the converse is not true in general: if all generalized Wronskians vanish, this does not imply that the functions are linearly dependent. However, the converse is true in many special cases. For example, if the functions are polynomials and all generalized Wronskians vanish, then the  functions are linearly dependent. Roth used this result about generalized Wronskians in his proof of Roth's theorem. For more general conditions under which the converse is valid see .