At a point of twice differentiability the first-order coefficient is the gradient; a function of class is twice differentiable at every point, with the usual gradient and Hessian; and at a local maximum the gradient vanishes and the Hessian is negative semidefinite.
We work in the setting of Product Measures and Lebesgue Measure on ℝⁿ: Standing Notation and Background, whose notation is fixed for every dimension and is used here with a natural number satisfying : the real numbers, and the Euclidean norm , dot product, distance and notion of openness, are as fixed there. Write for the set of symmetric real matrices, for the matrix-vector product, for the real matrix all of whose entries are , which belongs to , and for the positive semidefinite ordering on .
Let be open and let . Twice differentiability at a point is as defined there. Then the following hold.
1. (The first-order coefficient is the gradient) Suppose is twice differentiable at with first-order coefficient and Hessian . Then is differentiable at with derivative matrix the real matrix with one row and columns whose entry in row and column is the th coordinate of . Consequently all partial derivatives of exist at and is the gradient .
2. (Functions of class ) Suppose is of class on . Then is twice differentiable at every , with first-order coefficient the gradient and Hessian the Hessian matrix , which belongs to by claim 2 of Equality of Mixed Partial Derivatives, and Symmetry and Continuity of the Hessian. The two uses of the notation therefore agree.
3. (Second-order condition at a local maximum) Suppose is twice differentiable at with first-order coefficient and Hessian , and that has a local maximum at relative to . Then
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