Past exam of the optimisation for data science course of the University of Oxford / 2023 / 2 / b

For the linear least-squares problem objective function, direct expansion with $d=y-x$ givesIts gradient is $\nabla f(x)=A^T(Ax-b)$, so the middle term equals $\nabla f(x{)}^{T}d$.

Use the supplied thin singular value decomposition $A=US{V}^T$. Since $U^{T}U=I_n$, the linear map $w\mapsto Uw$ preserves the Euclidean norm. The square matrix $V$ is an orthogonal matrix, so $\Vert V^{T}d\Vert =\Vert d\Vert$. ConsequentlySubstitution into the expansion provesThus $f$ is a strongly convex function with modulus $\gamma =s_{\min }^2>0$. This modulus is sharp: choose $d=V{e}_i$ for an index attaining the smallest singular value. The Hessian matrix is $A^{T}A=V{S}^2{V}^T$, so the same conclusion says that its smallest eigenvalue is $s_{\min }^2$.