I am trying to minimize a convex objective numerically using gradient descent. I select the starting point randomly. I repeat the experiment multiple times. The optimal objective value I get each time is quit the same, but the minimizer is very different. Is it natural? How should it be handled in experiments?
2026-04-01 16:06:35.1775059595
unstable optimizer, stable objective
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Yes, this is entirely possible. The fact that a problem is convex just guarantees that any local minimum is a global minimum. You can have many points that reach this global minimum. Intuitively, you can think of a function with a "flat" bottom, somewhat like a bath tub, so two points can be far away but still be minimal.