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If all the partial derivatives of a function are known (for example, with the gradient), then the antiderivatives can be matched via the above process to reconstruct the original function up to a constant. Unlike in the single-variable case, however, not every set of functions can be the set of all (first) partial derivatives of a single function.
A partial function arises from the consideration of maps between two sets X and Y that may not be defined on the entire set X.A common example is the square root operation on the real numbers : because negative real numbers do not have real square roots, the operation can be viewed as a partial function from to .
The derivative of an integrable function can always be defined as a distribution, and symmetry of mixed partial derivatives always holds as an equality of distributions. The use of formal integration by parts to define differentiation of distributions puts the symmetry question back onto the test functions , which are smooth and certainly ...
The function can be constructed from the partial computable function (,) described above and the s-m-n theorem: for each , () is the index of a program which computes the function (,). To complete the proof, let F {\displaystyle F} be any total computable function, and construct h {\displaystyle h} as above.