Being stuck in local minima or in a long shallow valley happens in optimization problems all the time, Isn’t this what simulated annealing and similar techniques are designed to correct? I’ve seen this in maximum likelihood Markov chain discovery problems a lot.
I expect this problem would show up in any less-than-perfect optimizer, including SA variants. Heck, the metabolic example is basically the physical system which SA was based on in the first place. But it would look different with different optimizers, mainly depending on what the optimizer “sees” and what’s needed to “hide” information from it.
Being stuck in local minima or in a long shallow valley happens in optimization problems all the time, Isn’t this what simulated annealing and similar techniques are designed to correct? I’ve seen this in maximum likelihood Markov chain discovery problems a lot.
I expect this problem would show up in any less-than-perfect optimizer, including SA variants. Heck, the metabolic example is basically the physical system which SA was based on in the first place. But it would look different with different optimizers, mainly depending on what the optimizer “sees” and what’s needed to “hide” information from it.