BestSoFar... wanted behavior ?

[thibmonsel]

Hi Patrick,

I suppose, that the BestSoFar... solvers are working as intended.

import jax.numpy as jnp
import optimistix as optx

def f(x, _):
    return 1 / 2 * x

solv = optx.FixedPointIteration(10e-3, 10e-3)
sol = optx.fixed_point(f, solv, jnp.array(0.3), max_steps=10)
print(sol.value)


solv2 = optx.BestSoFarFixedPoint(solv)
sol2 = optx.fixed_point(f, solv2, jnp.array(0.3), max_steps=3) # shoots out error
print(sol2.value) 

solv2 returns an XlaRuntimeError but could it be possible instead to return the best so far value computed (here it would be at max_steps=2) even if the latest value of sol2 doesn't satisfy there termination condition ?

If not, would there be a possibility to get the best so for value computed without throwing a runtime error ?

[patrick-kidger]

Yup -- pass fixed_point(..., throw=False).