Convex optimization with Ridge regression

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If this is the constrained version of ridge regression:

min w∈Rd ∥Φw − y∥^2 s.t. ∥w∥2 ≤ s, (1)

where Φ ∈ R n×d and y ∈ R n . Answer the following questions.

•How can we prove that this is a convex optimization problem.

•Does strong duality really hold? If yes, derive the KKT condition regarding the optimal solution w∗ for the above problem.

• Does a closed-form solution exist? If yes, derive the closed-form solution. If not, can you propose an algorithm for computing the optimal solution (describe the key steps of your algorithm)? Please Help