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sritchie/minimize.cljc

Last active Sep 3, 2020
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Nelder-Mead multidimensional minimization in Clojure, based on Colin Smith's excellent work!
(ns sicmutils.numerical.minimize
"Implementations of minimization algorithms for single and multiple dimensions."
(:require [sicmutils.util :as u]
[sicmutils.util.stopwatch :as us]
[taoensso.timbre :as log])
#?(:clj
(:import (org.apache.commons.math3.optim.univariate
BrentOptimizer
UnivariateObjectiveFunction
SearchInterval
UnivariatePointValuePair)
(org.apache.commons.math3.analysis
UnivariateFunction)
(org.apache.commons.math3.optim.nonlinear.scalar
GoalType
ObjectiveFunction)
(org.apache.commons.math3.optim
MaxEval
OptimizationData
ConvergenceChecker))))
(defn- v+
"add two vectors elementwise."
[l r]
(mapv + l r))
(defn- v-
"subtract two vectors elementwise."
[l r]
(mapv - l r))
(defn- v*
"multiply vector v by scalar s."
[s v]
(mapv #(* s %) v))
(defn ^:private initial-simplex
"Takes an n-vector x0 and returns a list of n+1 n-vectors, of which x0 is the
first, and the remainder are formed by perturbing each coordinate in turn."
[x0 {:keys [nonzero-delta zero-delta]
:or {nonzero-delta 0.05
zero-delta 0.00025}}]
(let [x0 (vec x0)
scale (inc nonzero-delta)
f (fn [i xi]
(let [perturbed (if (zero? xi)
zero-delta
(* scale xi))]
(assoc x0 i perturbed)))]
(into [x0] (map-indexed f x0))))
(defn ^:private sup-norm
"Returns the absolute value of the distance of the individual coordinate in any
simplex farthest from its corresponding point in x0."
[[x0 :as simplex]]
(let [coords (if (sequential? x0)
(mapcat #(v- % x0) simplex)
(map #(- % x0) simplex))]
(reduce max (map u/compute-abs coords))))
(defn ^:private counted
"Takes a function and returns a pair of:
- an atom that keeps track of fn invocation counts,
- the instrumented fn"
[f]
(let [count (atom 0)]
[count (fn [x]
(swap! count inc)
(f x))]))
(defn ^:private sort-by-f
"Returns the two inputs `simplex` and `f(simplex)` sorted in ascending order by
function value.
Dimension must == the length of each element in the simplex."
([simplex f-simplex]
(sort-by-f simplex f-simplex (count (peek simplex))))
([simplex f-simplex dimension]
(let [indices-by-f (sort-by (partial nth f-simplex)
(range 0 (inc dimension)))
sorted-simplex (mapv simplex indices-by-f)
sorted-fsimplex (mapv f-simplex indices-by-f)]
[sorted-simplex sorted-fsimplex])))
(defn ^:private step-defaults
"Generates the options required for a step of Nelder-Mead. :adaptive? controls
the set of defaults. If true, they're generated using the supplied dimension;
else, they're static.
"
[dimension {:keys [alpha beta sigma gamma adaptive?] :as m}]
(let [base (if adaptive?
{:alpha 1.0
:beta (+ 1.0 (/ 2.0 dimension))
:gamma (- 0.75 (/ (* 2.0 dimension)))
:sigma (- 1.0 (/ dimension))}
{:alpha 1.0
:beta 2.0
:gamma 0.5
:sigma 0.5})]
(merge base (select-keys m [:alpha :beta :gamma :sigma]))))
(defn ^:private step-fn
"Returns a function that performs a single step of nelder-mead. The function
expects a sorted simplex and f-simplex, and returns sorted results - a pair of
- [simplex, f(simplex)]
[This Scholarpedia
page](http://www.scholarpedia.org/article/Nelder-Mead_algorithm) provides a
nice overview of the algorithm.
The parameters in opts follow the convention from [Gao and Han's
paper](https://www.researchgate.net/publication/225691623_Implementing_the_Nelder-Mead_simplex_algorithm_with_adaptive_parameters)
introducing the adaptive parameter version of Nelder-Mead:
:alpha - reflection cefficient
:beta - expansion coefficient
:gamma - contraction coefficient
:sigma - shrink coefficient
"
([f dimension opts]
(let [{:keys [alpha beta sigma gamma]} (step-defaults dimension opts)]
(letfn [(centroid-pt [simplex]
(v* (/ dimension) (reduce v+ (pop simplex))))
;; Returns the point generated by reflecting the worst point across
;; the centroid of the simplex.
(reflect [simplex centroid]
(v- (v* (inc alpha) centroid)
(v* alpha (peek simplex))))
;; Returns the point generated by reflecting the worst point across
;; the centroid, and then stretching it in that direction by a factor
;; of beta.
(reflect-expand [simplex centroid]
(v- (v* (inc (* alpha beta)) centroid)
(v* (* alpha beta) (peek simplex))))
;; Returns the point generated by reflecting the worst point, then
;; shrinking it toward the centroid by a factor of gamma.
(reflect-contract [simplex centroid]
(v- (v* (inc (* gamma alpha)) centroid)
(v* (* gamma alpha) (peek simplex))))
;; Returns the point generated by shrinking the current worst point
;; toward the centroid by a factor of gamma.
(contract [simplex centroid]
(v+ (v* (- 1 gamma) centroid)
(v* gamma (peek simplex))))
;; Returns a simplex generated by scaling each point toward the best
;; point by the shrink factor $\sigma$; ie, by replacing all
;; points (except the best point $s_1$) with $s_i = s_1 + \sigma (\s_i
;; - s_1)$.
(shrink [[s0 & rest]]
(let [scale-toward-s0 #(v+ s0 (v* sigma (v- % s0)))
s (into [s0] (map scale-toward-s0 rest))]
(sort-by-f s (mapv f s) dimension)))]
(fn [simplex [f-best :as f-simplex]]
(let [swap-worst (fn [elem f-elem]
(let [s (conj (pop simplex) elem)
fs (conj (pop f-simplex) f-elem)]
(sort-by-f s fs dimension)))
f-worst (peek f-simplex)
f-butworst (peek (pop f-simplex))
centroid (centroid-pt simplex)
reflected (reflect simplex centroid)
fr (f reflected)]
(cond
;; If the reflected point is the best (minimal) point so far, replace
;; the worst point with either an expansion of the simplex around that
;; point, or the reflected point itself.
;;
;; f(reflected worst) < f(best)
(< fr f-best)
(let [expanded (reflect-expand simplex centroid)
fe (f expanded)]
(if (< fe fr)
(swap-worst expanded fe)
(swap-worst reflected fr)))
;; f(best) <= f(reflected worst) < f(second worst)
;;
;; Else, if the reflected worst point is better than the second worst
;; point, swap it for the worst point.
(< fr f-butworst)
(swap-worst reflected fr)
;; f(butworst) <= f(reflected worst) < f(worst)
;;
;; If the reflected point is still better than the worst point,
;; generated a point by shrinking the reflected point toward the
;; centroid. If this is better than (or equivalent to) the reflected
;; point, replace it. Else, shrink the whole simplex.
(< fr f-worst)
(let [r-contracted (reflect-contract simplex centroid)
frc (f r-contracted)]
(if (<= frc fr)
(swap-worst r-contracted frc)
(shrink simplex)))
;; f(worst) <= f(reflected worst)
;;
;; Else, attempt to contrast the existing worst point toward the
;; centroid. If that improves performance, swap the new point; else,
;; shrink the whole simplex.
:else
(let [contracted (contract simplex centroid)
fc (f contracted)]
(if (< fc f-worst)
(swap-worst contracted fc)
(shrink simplex))))))))))
(defn ^:private stop-fn
"Returns a function that returns true if stopping conditions are satisfied by
its supplied iteration count, simplex and evaluated simplex, false otherwise."
[f-counter dimension {:keys [maxiter maxfun simplex-tolerance fn-tolerance]
:or {simplex-tolerance 1e-4
fn-tolerance 1e-4}}]
(let [maxiter (or maxiter (* dimension 200))
maxfun (or maxfun (* dimension 200))]
(fn [iterations simplex f-simplex]
(or (> iterations maxiter)
(> @f-counter maxfun)
(and (<= (sup-norm simplex) simplex-tolerance)
(<= (sup-norm f-simplex) fn-tolerance))))))
(defn multidimensional-minimize
"Find the minimum of the function f: R^n -> R, given an initial point q ∈ R^n.
Supports the following optional keyword arguments:
:callback if supplied, the supplied fn will be invoked with the intermediate
points of evaluation.
:info if true, wraps the result with evaluation information.
:adaptive? if true, the Nelder-Mead parameters for contraction, expansion,
reflection and shrinking will be set adaptively, as functions of the number of
dimensions. If false they stay constant.
:alpha sets the reflection coefficient used for each step of Nelder-Mead.
:beta sets the expansion coefficient used for each step of Nelder-Mead.
:gamma sets the contraction coefficient used for each step of Nelder-Mead.
:sigma sets the shrink coefficient used for each step of Nelder-Mead.
:maxiter Maximum number of iterations allowed for the minimizer. Defaults to
200*dimension.
:maxfun Maximum number of times the function can be evaluated before exiting.
Defaults to 200*dimension.
:simplex-tolerance When the absolute value of the max difference between the
best point and any point in the simplex falls below this tolerance, the
minimizer stops. Defaults to 1e-4.
:fn-tolerance When the absolute value of the max difference between the best
point's function value and the fn value of any point in the simplex falls
below this tolerance, the minimizer stops. Defaults to 1e-4.
:zero-delta controls the value to which 0 entries in the initial vector are
set during initial simplex generation. Defaults to 0.00025.
:nonzero-delta factor by which entries in the initial vector are perturbed to
generate the initial simplex. Defaults to 0.05.
See Gao, F. and Han, L.
Implementing the Nelder-Mead simplex algorithm with adaptive
parameters. 2012. Computational Optimization and Applications.
51:1, pp. 259-277
I gratefully acknowledge the [Python implementation in
SciPy](https://github.com/scipy/scipy/blob/589c9afe41774ee96ec121f1867361146add8276/scipy/optimize/optimize.py#L556:5)
which I have imitated here.
"
[func x0 & {:keys [callback info]
:or {callback (constantly nil)}
:as opts}]
(let [dimension (count x0)
[f-counter f] (counted func)
step (step-fn f dimension opts)
stop? (stop-fn f-counter dimension opts)
simplex (initial-simplex x0 opts)
f-simplex (mapv f simplex)]
(loop [[[s0 :as simplex] [f0 :as f-simplex]] (sort-by-f simplex f-simplex dimension)
iteration 0]
(callback s0)
(if (stop? iteration simplex f-simplex)
(if info
{:result s0
:value f0
:iterations iteration
:fncalls @f-counter}
s0)
(recur (step simplex f-simplex)
(inc iteration))))))
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@littleredcomputer littleredcomputer commented Sep 3, 2020

I like this better, and I'm glad I stopped when I did (before writing a bunch of doc) since I agree with 99% of what you have here.

I'm going to push an update to my PR which just changes the interface: introducing another function layer. But beyond the structure, I think what you have is fine.

What do you think of wrapping the let in step-fn with sort-by-f so that no one can forget to call it? There are probably circumstances where it can be omitted, but that would be awfully delicate.

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@sritchie sritchie commented Sep 3, 2020

@littleredcomputer, for sure, I did that since in the current version, we have the first stop? check before the first step, and I was trying to prematurely optimize for calling sort twice that first time... but that is silly.

We could either:

  • add the sort at the beginning, as you suggest (though I think we also need it in the return, otherwise the call to stop? will catch an unsorted return on the final iteration.
  • OR, we could add a precondition check that f-simplex is sorted, which would save us a sort per loop.

Your call, but yes, I agree that this would be very tough to debug.

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@littleredcomputer littleredcomputer commented Sep 3, 2020

Thank you for this, it has been helpful for the rust (I had in fact completely forgotten about map-indexed, peek, etc.) I say roll forward with whatever you think is best here.

From the war-story POV I have found it useful when porting these kind of things to be able to compare step-output with a reference implementation and I think you have preserved that. With the "split" interface we gain the sciPy like ability to eventually add an :algorithm :m/nelder-mead option, and we can leave nelder-mead public so that (if we felt like it) we could bring over more of the sciPy tests that make assertions about the values in the result dict

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@littleredcomputer littleredcomputer commented Sep 3, 2020

And furthermore I really like that we're doing this Rich Hickey style and not messing with transients. It feels fast enough and it benefits from functional style in terms of presentation and for the transparency of the implementation vs. the paper.

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