I came across a concept in Boyd's book "Convex Optimization" that I'm struggling to grasp intuitively. The concept is stated as follows:
-If λ ≻K∗ 0 and x minimizes λ^T.z over z ∈ S, then x is minimal.
-a point x can be minimal in S, but not a minimizer of λ^T.z over z ∈ S, for any λ
I understand the mathematical notation but would appreciate an intuitive explanation.