In the mathematical fields of partial differential equations and geometric analysis, the maximum principle is any of a collection of results and techniques of fundamental importance in the study of elliptic and parabolic differential equations. In the simplest case, consider a function of two variables such that The weak maximum principle, in this setting, says that for any open precompact subset of the domain of , the maximum of on the closure of is achieved on the boundary of . The strong maximum principle says that, unless is a constant function, the maximum cannot also be achieved anywhere on itself. Such statements give a striking qualitative picture of solutions of the given differential equation. Such a qualitative picture can be extended to many kinds of differential equations. In many situations, one can also use such maximum principles to draw precise quantitative conclusions about solutions of differential equations, such as control over the size of their gradient. There is no single or most general maximum principle which applies to all situations at once. In the field of convex optimization, there is an analogous statement which asserts that the maximum of a convex function on a compact convex set is attained on the boundary. Intuition A partial formulation of the strong maximum principle Here we consider the simplest case, although the same thinking can be extended to more general scenarios. Let be an open subset of Euclidean space and let be a function on such that where for each and between 1 and , is a function on with . Fix some choice of in . According to the spectral theorem of linear algebra, all eigenvalues of the matrix are real, and there is an orthonormal basis of consisting of eigenvectors. Denote the eigenvalues by and the corresponding eigenvectors by , for from 1 to . Then the differential equation, at the point , can be rephrased as The essence of the maximum principle is the simple observation that if each eigenvalue is positive (which amounts to a certain formulation of "ellipticity" of the differential equation) then the above equation imposes a certain balancing of the directional second derivatives of the solution. In particular, if one of the directional second derivatives is negative, then another must be positive. At a hypothetical point where is maximized, all directional second derivatives are automatically nonpositive, and the "balancing" represented by the above equation then requires