The relaxed knapsack problem is a linear program, which is itself convex and has a global optimum; but once the variables are rewritten through nonlinear parameterization such as sigmoid, or non-convex regularization terms are added, the optimization landscape is no longer convex and gradient descent may stop at a local optimum.