tensorflow / tensorflow/probability
Fixed step-size ODE solver
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Description
There currently appears to be no fixed-step size ODE solver in math.ode. Are there any plans to add one? It seems like it would be fairly easy to modify the Dormand-Prince code to do it.
The reason I'm interested in one is that I think I'm running into problems doing minimization with DormantPrince due to the changing size of the steps. The calculated gradient the final state w.r.t some driving parameters fluctuates a little with changing step size, which I think is enough to throw off the BFGS optimization. I can reduce the problem to some extent by 'encouraging' fixed-step like behaviour using the DormandPrince options, but this doesn't seem a very good solution long term.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the existing math.ode Dormand-Prince implementation and its options to determine how step sizes are currently selected. Define the fixed-step solver behavior and how it should support optimization with BFGS, then verify the solver produces consistent final-state gradients across runs.
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Assessment
- Tech stack
- tensorflow
- Domain
- backend
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100