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3 changes: 3 additions & 0 deletions COPYING
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This project is dual-licensed under the Apache 2.0 and MIT licenses.

You may use this code under the terms of either license.
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Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/

TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

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Copyright 2021 Daniel Vainsencher and Daniel Fox

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

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MIT License

Copyright (c) 2021 Daniel Vainsencher and Daniel Fox

Permission is hereby granted, free of charge, to any person obtaining a copy
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SOFTWARE.
111 changes: 7 additions & 104 deletions README.md
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# You have a numerical problem.
Iterative methods and associated utilities in Rust.

## You applied an iterative method naively, now you have 10 problems that recur forever.

Suppose we have a problem, and initial solution (probably bad) and a
function f such that f(x) is a better solution than x by some numeric
cost measure g. Then applying f repeatedly gives us a sequence of ever
better solutions, hopefully converging to an optimal solution. The
most common initial "I want to see this work!" implementation looks
like:

x = x0
for i in 100:
x = f(x)

But as soon as you've implemented such a method, many additional
considerations come up:
- are 100 iterations enough?
- are x actually ever improving?
- how long is each iteration taking?
- perhaps we want to log every 10th x for later plotting?
- it crashed after 2 days of iterations, perhaps I should have saved
intermediate iterations...

Following the path of least resistance, we might add `print(g(x))` at
the end, move `print(g(x))` into the loop, use timers and print
differences, etc. In general, we find ourselves adding a few lines of
code, then moving, deleting, re-adding, commenting them out... and on
the next project perhaps copy and pasting them again.

Some of the time, answering our questions require access to internal
state of f, which therefore gets inlined into the loop, and now all
these external concerns are mixed with the algorithm, possibly adding
bugs to the very algorithm they were supposed to help understand!

Argh!

## Can't we just write the algorithm once, write each utility once, and somehow do only the wiring together we need?

Yes, kind of, but it is language specific. LT;DR:

* f should not be a black box that transforms solutions x; it should
instead take and returns a struct s with all interesting state of
the algorithm.
* To start the sequence of states, convert a problem p into an initial
state s0
* Abstract the repeated application of f as a stream of states s,
assuming your language has one.
* Abstract the utilities as stream adaptors. Such a utility takes a
stream and applies to each a transformation (e.g., measure value), a
side effect (log some states) or both.

I did not invent this approach, and will happily add further
references!; briefly, this hope has a long history of programming
language specific ([a Haskell mailing list circa
2006](https://mail.haskell.org/pipermail/haskell-cafe/2006-August/017394.html))
answers. A recent and nice entry tackling this problem in Julia is
"[Iterative methods done right (because life is too short to write
for-loops)](http://lostella.github.io/2018/07/25/iterative-methods-done-right.html)"
(referred to as IM below) proposes to write iterative methods as Julia
iterators and various utilities for them as iterator adaptors. I
recommend reading their exposition, which inspired this project!

# How deep can we follow this path in rust?

Rust seems promising for many reasons.

* Rust loves abstracting sequences; iterators are a (the?) first class
citizen in, e.g., for loops.
* In Rust abstraction costs are low, so won't dominate the work even
when iterations are pretty cheap.
* It is a language essentially optimized for writing high efficiency
reusable abstractions, so when iterator is not an exact fit, we can
use a variation.

## The simplest thing and how it fails

In simple cases, Rust iterators will do. We even have some nice little
adaptors pre-made, like `enumerate` with annotates our state with its
location in the stream, and `take` which stops after the given
iteration number:

for (i, state) in convert_problem_to_iterator(problem).enumerate().take(4) {
println!("Iteration {} has state {}", i, state);
}


# TODO here
* rework the fib example, can we actually output the state instead of
the next value?
* Understand rustdoc better. Link to the fib code examples? Does it
make sense to make this whole essay literate code using rustdoc?

And we can go quite far with this direction, as long as state is cheap
to copy. The caveat is because iterators return values, not
references, and so for the iterator to own them and the loop body and
down stream adaptors to have access requires copies. Some might be
optimized away by a [sufficiently smart
compiler](https://wiki.c2.com/?SufficientlySmartCompiler) and Rust is
plenty smart, but the Rusty approach to reliable efficiency is to
minimize copies and make them explicit.

Some unprocessed links:

1. https://docs.rs/streaming-iterator/0.1.5/streaming_iterator/
# Licensing

This project is dual-licensed under the [Apache](LICENSE_APACHE.md)
and [MIT](LICENSE_MIT.md) licenses. You may use this code under the
terms of either license. Contributing to this repo in any form
constitutes agreement to license any such contributions under all
licenses specified in the COPYING file at that time.

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