New Root Solver: ITP Method #544
Conversation
| max, | ||
| tol, | ||
| // kappa1, suggested from paper | ||
| float!(0.2) / (max - min), |
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Wondering, maybe we want to have this function return Result and verify max - min cannot be zero so we don't panic here. Not sure though, maybe not worth it right now.
| let sol = (self.a + self.b) * float!(0.5); | ||
| // TODO: This function evaluation serves no purpose other than to serve argmin's cost | ||
| // method on the state. It feels wasteful. | ||
| let f_sol = problem.cost(&sol)?; |
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This bothers me, but maybe someone smarter than me might know a clever way to not do this.
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #544 +/- ##
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+ Coverage 91.92% 91.94% +0.01%
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Files 177 178 +1
Lines 23724 23940 +216
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+ Hits 21808 22011 +203
- Misses 1916 1929 +13 ☔ View full report in Codecov by Sentry. |
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@stefan-k Does this seem like a worthwhile addition? If so, I did take a peek at CI here and some of these seem like flakes- I could totally be misreading, though. |
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Hi @duncanam , this is definitely a worthwhile addition, thank you! Unfortunately it will take a bit until I can give a useful review, hopefully around the upcoming holidays. Apologies for being so unresponsive recently :/ |
No worries! Have a good holiday season! |
Intro
Hello! I noticed that this lovely crate does not have an implementation of the ITP method root solver, which is, quoting Wikipedia:
"The ITP method, short for Interpolate Truncate and Project, is the first root-finding algorithm that achieves the superlinear convergence of the secant method while retaining the optimal worst-case performance of the bisection method. It is also the first method with guaranteed average performance strictly better than the bisection method under any continuous distribution. In practice it performs better than traditional interpolation and hybrid based strategies (Brent's Method, Ridders, Illinois), since it not only converges super-linearly over well behaved functions but also guarantees fast performance under ill-behaved functions where interpolations fail."
Practically, it is faster than
BrentRootdue to fewer function evaluations. I thought this might be a handy contribution towardsargmin, since I didn't see too many implementations of this online (There's theitppackage in R, and in Julia I think it exists inRoots.jl. I also found it located within the Rust cratekurbohere, but this was tuned specifically for curve fitting instead of generic root solving. I templated this implementation based off ofbrentroot.rs.Verification
I used the quadratic test from
brentroot.rs, and it achieved a solution in one iteration faster. However, there's an additional function evaluation that occurs to satisfy theargminiteration state, so currently comes in equal in terms of function evaluations with BrentRoot for that example specifically.Additionally, I added a test against the polynomial shown in the example on Wikipedia, and stepping through in a debugger I was able to verify that
aandbbounds on the bracket (of the solver) indeed match what is in that table there.Let me know what else should be changed, as I'm new to this community. Thanks! 😃