Lagrange [WIP] #31
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Lagrange [WIP] #31
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teddygroves
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Dec 10, 2024
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| S = model.structure.S | ||
| dG = (S.T @ model.parameters.dgf + 2.4788191*[email protected](conc)) |
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can these lines be deleted?
teddygroves
reviewed
Dec 10, 2024
src/enzax/steady_state.py
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| lagrangian, | ||
| solver, | ||
| jnp.concat([jnp.log(guess), lambda_guess]), |
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This is more readable and can avoid annoying copy/paste bugs later:
Suggested change
| lagrangian, | |
| solver, | |
| jnp.concat([jnp.log(guess), lambda_guess]), | |
| fn=lagrangian, | |
| solver=solver, | |
| y0=jnp.concat([jnp.log(guess), lambda_guess]), |
teddygroves
reviewed
Dec 10, 2024
src/enzax/steady_state.py
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| def lagrangian( | ||
| z: Float[Array, " n_balanced*2"], | ||
| model: RateEquationModel, | ||
| ) -> Float[Array, " n_balanced*2"]: |
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Can you add a docstring for this function?
teddygroves
reviewed
Dec 10, 2024
| ) -> Float: | ||
| S = model.structure.S | ||
| dG = (S.T @ model.parameters.dgf + 2.4788191*[email protected](conc)) | ||
| return sum(jnp.square(model.dcdt(0, x))) |
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maybe either jnp.square(model.dcdt(0, x)).sum() or jnp.sum(jnp.square(model.dcdt(0, x))) would be better here - I'm not sure exactly what happens when you use the standard sum
teddygroves
requested changes
Dec 10, 2024
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Summary
Steady-state solving is difficult and errors are problematic. This approach is not necessarily better than the method posed in diffrax but it has the potential to fail in a detectable way; this may be desirable for mcmc. Preliminary tests suggest that is may be faster than standard ODE solving, up to 3 times faster for the methionine cycle “BAD_GUESS” test case, and it achieves steady state.
Checklist:
README.mdup to date