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Separation of variables may be possible in some coordinate systems but not others, [2] and which coordinate systems allow for separation depends on the symmetry properties of the equation. [3] Below is an outline of an argument demonstrating the applicability of the method to certain linear equations, although the precise method may differ in ...
Let (,) = be a well-posed problem, i.e. : is a real or complex functional relationship, defined on the cross-product of an input data set and an output data set , such that exists a locally lipschitz function : called resolvent, which has the property that for every root (,) of , = ().
In the 2×2 case, if the coefficient determinant is zero, then the system is inconsistent if the numerator determinants are nonzero, or indeterminate if the numerator determinants are zero. For 3×3 or higher systems, the only thing one can say when the coefficient determinant equals zero is that if any of the numerator determinants are nonzero ...
If X is a nonnegative random variable and a > 0, and U is a uniformly distributed random variable on [,] that is independent of X, then [4] (). Since U is almost surely smaller than one, this bound is strictly stronger than Markov's inequality.