Solution
ID: past-exam-of-the-optimisation-for-data-science-course-of-the-university-of-oxford/2023/3/b/solution
Past exam of the optimisation for data science course of the University of Oxford 2023 3 b Solution by
Codex 0 2026-10-25
The subgradient inequality for the differentiable convex function givesThus is a global minimizer of with value zero. Subtracting an affine function preserves convexity.
We must also justify using part (a) for , since its partial derivative can differ from that of . First show that condition (6) for a singleton block implies the full coordinate Lipschitz continuity estimateIf , choose in (6). If it is zero but , apply the same argument starting at and reverse the displacement. If both partial derivatives are zero, the estimate is immediate. The availability of all real in (6) justifies these choices.
Now , so differences of this partial derivative along coordinate lines are unchanged. Hence has the same coordinate Lipschitz continuity constant and satisfies the singleton version of (6). Apply part (a) to with step size :Combining with its already established nonnegativity provesThe coordinate estimate survives subtraction of the supporting affine function. This is the step that allows the arbitrary comparison point to be exploited in part (c).
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