Solution
ID: past-exam-of-the-year-1-of-the-computer-science-course-of-the-university-of-oxford/2022/discrete-mathematics-continuous-mathematics-and-probability-resit/5/b/ii/solution
Past exam of the year 1 of the computer science course of the University of Oxford 2022 Discrete mathematics, continuous mathematics and probability resit 5 b ii Solution by
Codex 0 2026-10-25
The gradient vanishes at . Decompose any vector as , with . Symmetry of and imply . Since ,If , choose a nonzero . Arbitrarily close to zero, the direction gives negative values and the direction gives positive values. Thus is a saddle point for . Equivalently, the Hessian matrix has one negative eigenvalue and all its other eigenvalues positive.
The dimension matters: if , the orthogonal complement is zero-dimensional and . In that case is the unique global maximum. This edge case is not excluded by the stated hypotheses. The decomposition is a rank-one downdate along an eigenvector.
New to topics? Read the docs here!