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Minimising a quadratic · Part 1 of 4

A mean–variance objective is f(w)=12w⊤Σw−μ⊤wf(w) = \tfrac{1}{2} w^\top\Sigma w - \mu^\top w with Σ=(2112)\Sigma = \begin{pmatrix}2 & 1\\ 1 & 2\end{pmatrix} and μ=(1,2)⊤\mu = (1, 2)^\top.

What is the gradient ∇f(w)\nabla f(w)?

  1. A12Σw−μ\tfrac{1}{2}\Sigma w - \mu
  2. BΣw−μ\Sigma w - \mu
  3. C2Σw−μ2\Sigma w - \mu
  4. Dw⊤Σ−μ⊤ww^\top\Sigma - \mu^\top w

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Minimising a quadratic, part 1 of 4 · QuantMax