Quadratic Functions, Optimization, and Quadratic Forms
4 (GP) : minimize f (x) s.t. x ∈ n, where f (x): n → is a function. We often design algorithms for GP by building a local quadratic model of f (·)atagivenpointx =¯x.We form the gradient ∇f (¯x) (the vector of partial derivatives) and the Hessian H(¯x) (the matrix of second partial derivatives), and approximate GP by the following problem which uses the Taylor expansion of f (x)atx ...
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