A Measure Of Borel Weight

In the last few posts we have implemented a type to represent Borel sets of the real numbers, which are the subsets of them that can be created with countable unions of intervals with closed or open lower and upper bounds. Whilst I would argue that doing so was a worthwhile exercise in its own right, you may be forgiven for wondering what Borel sets are actually for and so in this post I shall try to justify the effort that we have spent on them.

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A Borel Universe

Last time we took a look at Borel sets of real numbers, which are subsets of the real numbers that can be represented as unions of countable sets of intervals Ii. We got as far as implementing the ak.borelInterval type to represent an interval as a pair of ak.borelBound objects holding its lower and upper bounds.
With these in place we're ready to implement a type to represent Borel sets and we shall do exactly that in this post.

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A Decent Borel Code

A few posts ago we took a look at how we might implement various operations on sets represented as sorted arrays, such as the union, being the set of every element that is in either of two sets, and the intersection, being the set of every element that is in both of them, which we implemented with ak.setUnion and ak.setIntersection respectively.
Such arrays are necessarily both finite and discrete and so cannot represent continuous subsets of the real numbers such as intervals, which contain every real number within a given range. Of particular interest are unions of countable sets of intervals Ii, known as Borel sets, and so it's worth adding a type to the ak library to represent them.

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The After Strife

As well as required arithmetic operations, such as addition, subtraction, multiplication and division, the IEEE 754 floating point standard has a number of recommended functions. For example finite determines whether its argument is neither infinite nor NaN and isnan determines whether its argument is NaN; behaviours that shouldn't be particularly surprising since they're more or less equivalent to JavaScript's isFinite and isNaN functions respectively.
One recommended function that JavaScript does not provide, and which I should like to add to the ak library, is nextafter which returns the first representable floating point number after its first argument in the direction towards its second.

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In the last couple of posts we have seen various ways to partially or fully sort data and the kinds of queries that we can run against them once they have been. Such query operations make fully sorted arrays a convenient way to represent sets, or more accurately multisets which treat repeated elements as distinct from each other, and in this post we shall exploit this fact to implement some operations that we might wish to perform upon them.

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I Still Haven't Found What I'm Looking For

Last time we took a look at a selection of sorting operations that we can use to sort arrays, or ranges of elements within them. After defining some useful comparison functions satisfying JavaScript's requirement of returning a negative number when the first argument compares smaller than the second, zero when they compare equal and a positive number otherwise, and a function to map negative integers to indices read from the end of arrays in the same way that Array.slice does, we first implemented ak.partition which divides elements into two ranges; those elements that satisfy some given condition followed by those elements that don't. We saw how this could be used to implement the quicksort algorithm but instead defined ak.sort to sort a range of elements using Array.sort, slicing them out beforehand and splicing them back in again afterwards if they didn't represent whole arrays. We did use it, however, to implement ak.nthElement which puts a the correctly sorted element in a given position position within a range, putting before it elements that are no greater and after it elements that are no smaller. Finally, we implemented ak.partialSort which puts every element in a range up to, but not including, a given position into its correctly sorted place with all of the elements from that position onwards comparing no less than the last correctly sorted element.
This time we shall take a look at some of the ways that we can query data after we have manipulated it with these functions.

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We're All Sorted From A To Z

Something that I miss when programming in JavaScript is the wide variety of array manipulation functions available in my primary language, C++. We have, in fact, already implemented one of them with ak.shuffle which randomly rearranges the elements of an array. We shall be needing another one of them in the not too distant future and so I have decided to take a short break from numerical computing to add those of them that I use the most frequently to the ak library, starting with a selection of sorting operations.

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Do The Evolution

In the last few posts we have taken a look at genetic algorithms, which use simple models of biological evolution to search for global maxima of functions, being those points at which they return their greatest possible values.
These models typically represent the arguments of the function as genes within the binary chromosomes of individuals whose fitnesses are the values of the function for those arguments, exchange genetic information between them with a crossover operator, make small random changes to them with a mutation operator and, most importantly, favour the fitter individuals in the population for reproduction into the next generation with a selection operator.
We used a theoretical analysis of a simple genetic algorithm to suggest improved versions of the crossover operator, as well as proposing more robust schemes for selection and the genetic encoding of the parameters.
In this post we shall use some of them to implement a genetic algorithm for the ak library.

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The Best Laid Schemata

We have seen how we can exploit a simple model of biological evolution, known as a genetic algorithm, to search for global maxima of functions, being those points at which they return their greatest values.
This model treated the function being optimised as a non-negative measure of the fitness of individuals to survive and reproduce, replacing negative results with zero, and represented their chromosomes with arrays of bits which were mapped onto its arguments by treating subsets of them as integers that were linearly mapped to floating point numbers with given lower and upper bounds. It simulated sexual reproduction by splitting pairs of the chromosomes of randomly chosen individuals at a randomly chosen position and swapping their bits from it to their ends, and mutations by flipping randomly chosen bits from the chromosomes of randomly chosen individuals. Finally, and most crucially, it set the probability that an individual would be copied into the next generation to its fitness as a proportion of the total fitness of the population, ensuring that that total fitness would tend to increase from generation to generation.
I concluded by noting that, whilst the resulting algorithm was reasonably effective, it had some problems that a theoretical analysis would reveal and that is what we shall look into in this post.

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It's All In The Genes

Last time we took a look at the simulated annealing global minimisation algorithm which searches for points at which functions return their least possible values and which drew its inspiration from the metallurgical process of annealing which minimises the energy state of the crystalline structure of metals by first heating and then slowly cooling them.
Now as it happens, physics isn't the only branch of science from which we can draw inspiration for global optimisation algorithms. For example, in biology we have the process of evolution through which the myriad species of life on Earth have become extraordinarily well adapted to their environments. Put very simply this happens because offspring differ slightly from their parents and differences that reduce the chances that they will survive to have offspring of their own are less likely to be passed down through the generations than those that increase those chances.
Noting that extraordinarily well adapted is more or less synonymous with near maximally adapted, it's not unreasonable to suppose that we might exploit a mathematical model of evolution to search for global maxima of functions, being those points at which they return their greatest possible values.

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Gallimaufry

 AKCalc ECMA Endarkenment Turning Sixteen

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