CODEWITHSUNDEEP
openmdao
Chris
Chris

Reputation: 57

How do I warm start a dymos optimization problem?

My problem: I have a system with 4 states and 4 parameters (static) that I would like to optimize. The parameters are initialized to some known values that would result in trajectories that respect constraints. The states are initialized to a constant value. To verify the model, I run the problem where the parameters setting opt=False. Once verified, I rebuild the OpenMDAO problem with opt=True and run the optimizer.

I'm running a study to evaluate how each parameter affects the system, cost function, etc. and how the initial guess impacts the optimization (ideally, it doesn't). The problem I encounter is that some initial guesses for a parameter result in a failed optimization (iteration limit or positive line search) while others don't and it's not immediately clear why. Note: I always provide an initial guess for the problem that results in feasible trajectories. I check this by setting opt=False for the parameters when I build the problem. My assumption is that although my initial guess for the parameters are okay, my initial guess for the states is not and the problem gets stuck trying to get feasible trajectories.

My solution/idea: Is it possible to warm start an optimization problem in Dymos? To warm start, I would like to provide a feasible solution to the states and state rates of the optimizer. As a general flow I would like to first (1) run the optimization with the opt setting in controls and parameters set to False to get a state trajectory, then (2) set the opt setting for controls and parameters to True, and finally (3) re-run the optimization. It seems like there should be an easy way to do this, but I can't determine how without creating 2 problems (with different opt settings) and setting all the initial state guesses of the opt=True problem.

Note: I did read this post: Dymos how to use previous trajectory solution as initial guess? and I can rerun a problem. I just don't know how to change the opt setting between runs.

If there is an alternate or better solution to my problem, I'd be interested in that as well.

Upvotes: 0

Views: 283

Answers (1)

Mark Garnett
Mark Garnett

Reputation: 319

If you are using IPOPT, using a previous solution as your initial guess doesn't really help. This is due to the nature of interior point optimizers. On start, the barrier parameter mu is large. This will push the "optimum" solution, for that value of the barrier parameter mu, from doing Newton's method, AWAY from the initial guess. Then mu is decreased, Newton's method gets you closer to the true optimum. This process gets repeated as mu as decreased, until finally mu is small and you get back to the point, which was the optimum that you guessed initially.

Also, because we are using a Quasi-Newton method with a limited-memory Hessian approximation (L-BFGS) when going through Dymos/pyoptsparse, all the information about the Hessian is not there when you start again even if your initial guess is the optimum. So that information has to be filled in again as the algorithm iterates.

I am not an IPOPT expert but this seems to explain why I had no luck trying to use an "improved" initial guess. One thing that did help a lot with convergence was increasing the "limited_memory_max_history" parameter to 100 or so.

IPOPT does have the warm-start option but getting it the initial information it needs regarding the Hessian and initial multipliers might be something you have to go into pyoptsparse to figure out how to do.

Upvotes: 1

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