Monotone and antitone functions
(not over ℝ just the domain you see = 0<x<1⊂ℝ)
These are examples of invertible functions.
(Source: talizmatik)
Posts tagged with functions
Monotone and antitone functions
(not over ℝ just the domain you see = 0<x<1⊂ℝ)
These are examples of invertible functions.
(Source: talizmatik)

Three different ways of looking at a 𝔸²→𝔸¹ function:
Is this a simple or complex function? It has lots of discontinuities; it doesn’t correspond in any obvious way to any classical mathematical functions; to program it would surely take a lot of “arbitrary”, non-simple specifications. And yet it’s easily recognisable to any of us.
image by Gonzalez & Woods
(Source: class.coursera.org)

What does it mean to program in a functional style? (por Brian Will)
if, try, while return something
The λ-calculus is, at heart, a simple notation for functions and application. The main ideas are applying a function to an argument and forming functions by abstraction. The syntax of basic λ-calculus is quite sparse, making it an elegant, focused notation for representing functions. Functions and arguments are on a par with one another. The result is an intensional theory of functions as rules of computation, contrasting with the traditional extensional approach one of function as a set of pairs of a certain kind. Despite its sparse syntax, the expressiveness and flexibility of the λ-calculus make it a cornucopia of logic and mathematics. This entry develops some of the central highlights of the field and prepares the reader for further study of the subject and its applications in philosophy, linguistics, computer science, and logic.
Alama, Jesse, “The Lambda Calculus”, The Stanford Encyclopedia of Philosophy (Spring 2013 Edition), Edward N. Zalta (ed.),
via David A Edwards

What does it mean when mathematicians talk about a bijection or homomorphism?
Imagine you want to get from X to X′ but you don’t know how. Then you find a “different way of looking at the same thing” using ƒ. (Map the stuff with ƒ to another space Y, then do something else over in image ƒ, then take a journey over there, and then return back with ƒ ⁻¹.)
The fact that a bijection can show you something in a new way that suddenly makes the answer to the question so obvious, is the basis of the jokes on www.theproofistrivial.com.



In a given category the homomorphisms Hom ∋ ƒ preserve all the interesting properties. Linear maps, for example (except when det=0) barely change anything—like if your government suddenly added another zero to the end of all currency denominations, just a rescaling—so they preserve most interesting properties and therefore any linear mapping to another domain could be inverted back so anything you discover over in the new domain (image of ƒ) can be used on the original problem.
All of these fancy-sounding maps are linear:
They sound fancy because whilst they leave things technically equivalent in an objective sense, the result looks very different to people. So then we get to use intuition or insight that only works in say the spectral domain, and still technically be working on the same original problem.
Pipe the problem somewhere else, look at it from another angle, solve it there, unpipe your answer back to the original viewpoint/space.
For example: the Gaussian (normal) cumulative distribution function is monotone, hence injective (one-to-one), hence invertible.
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By contrast the Gaussian probability distribution function (the “default” way of looking at a “normal Bell Curve”) fails the horizontal line test, hence is many-to-one, hence cannot be totally inverted.
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So in this case, integrating once ∫[pdf] = cdf made the function “mathematically nicer” without changing its interesting qualities or altering its inherent nature.
“Going the long way” can be easier than trying to solve a problem directly.

Saying derivative is “slope” is a nice pedant’s lie, like the Bohr atom
which misses out on a deeper and more interesting later viewpoint:
The “slope” viewpoint—and what underlies it: the “charts” or “plots” view of functions as ƒ(x)–vs–x—like training wheels, eventually need to come off. The “slope” metaphor fails
ƒ(x)–vs–x, where is the “slope” there?“Really” it’s all about diff’s. Derivatives are differences (just zoomed in…this is what lim ∆x↓0 was for) and that viewpoint works, I think, everywhere.
I half-heartedly tried making these drawings in R with the barcode package but they came out ugly. Even uglier than my handwriting—so now enjoy the treat of my ugly handwriting.
Step back to Descartes’ definition of a function. It’s an association between two sets. And the language we use sounds “backwards” to that of English. If I say “associate a temperature number to every point over the USA”
then that should be written as a function ƒ: surface → temp.,
(or we could say ƒ: ℝ²→ℝ with ℝ²=(lat,long) )
The \to arrow and the “maps to” phrasing are backwards of the way we speak.

{elf, book, Kraken, 4^π^e} … no, I’m not sure where that came from either. But I think we can agree that such a set is unstructured.

Great. I drew above a set “without other structure” as the source (domain) and a branched, partially ordered weirdy thing as the target (codomain). Now it’s possible with some work to come up with a calculus like the infinitesimal one on ℝ→ℝ functions that’s taught to many 19-year-olds, but that takes more work. But for right now my point is to make that look ridiculous and impossible. Newton’s calculus is something we do only with a specific kind of Cartesian mapping: where both the from and the to have Euclidean concepts of straight-line-ness and distance has the usual meaning from maths class. In other words the Newtonian derivative applies only to smooth mappings from ℝ to ℝ.
Let’s stop there and think about examples of mappings.
(Not from the real world—I’ll do another post on examples of functions from the real world. For now just accept that numbers describe the world and let’s consider abstractly some mappings that associate, not arbitrarily but in a describable pattern, some numbers to other numbers.)


(I didn’t have a calculator at the time but the circle values for [1,2,3,4,5,6,7] are [57°,114°,172°,229°,286°,344°,401°=41°].)
I want to contrast the “map upwards” pictures to both the Cartesian pictures for structure-less sets
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and to the normal graphical picture of a “chart” or “plot”.
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Notice what’s obscured and what’s emphasised in each of the picture types. The plots certainly look better—but we lose the Cartesian sense that the “vertical” axis is no more vertical than is the horizontal. Both ℝ’s in ƒ: ℝ→ℝ are just the same as the other.
And if I want to compose mappings? As in the parabola picture above (first the square function, then an affine recentering). I can only show the end result of g∘ƒ rather than the intermediate result.
Whereas I could line up a long vertical of successive transformations (like one might do in Excel except that would be column-wise to the right) and see the results of each “input-output program”.
(Además, I have a languishing draft post called “How I Got to Gobbledegook” which shows how much simpler a sequence of transforms can be rather than “a forbidding formula from a textbook”.)
Another weakness of the “charts” approach is that whereas "Stay the same" command ought to be the simplest one (it’s a null command), it gets mapped to the 45˚ line:

Here’s the familiar parabola / x² plot “my way”: with the numbers written out so as to equalise the target space and the source space.

Now the “new” tool is in hand let’s go back to the calculus. Now I’m going to say “derivative=pulse” and that’s the main point of this essay.

Considering both the source ℝ→ and the target →ℝ on the same footing, I’ll call the length of the arrows the “mapping strength”. In a convex mapping like square the diffs are going to increase as you go to the right.

OK now in the middle of the piece, here is the main point I want to make about derivatives and calculus and how looking at numbers written on the paper rather than plots makes understanding a push forward possible. And, in my opinion, since in business the gigantic databases of numbers are commoner than charts making themselves, and in life we just experience stimuli rather than someone making a chart to explain it to us, this perspective is the more practical one.

I’m deliberately alliding the concepts of diff as
R’s diff function

Linear
I’m still not quite done with the “my style of pictures” because there’s another insight you can get from writing these mappings as a bar code rather than as a “chart”. Indeed, this is exactly what a rug plot does when it shows histograms.
Here are some strip plots = rug plots = carpet plots = barcode plots of nonlinear functions for comparison.


The main conclusion of calculus is that nonlinear functions can be approximated by linear functions. The approximation only works “locally” at small scales, but still if you’re engineering the screws holding a plane together, it’s nice to know that you can just use a multiple (linear function) rather than some complicated nonlineary thingie to estimate how much the screws are going to shake and come loose.
For me, at least, way too many years of solving y=mx+b obscured the fact that linear functions are just multiples. You take the space and stretch or shrink it by a constant multiple. Like converting a currency: take pesos, divide by 8, get dollars. The multiple doesn’t change if you have 10,000 pesos or 10,000,000 pesos, it’s still the same conversion rate.





So in a neighborhood or locality a linear approximation is enough. That means that a collection of linear functions can approximate a nonlinear one to arbitrary precision.

That means we can use computers!

Square
I can’t use the example of self times self so many times without exploring the concept a bit. Squares to me seem so limited and boring. No squizzles, no funky shapes, just boring chalkboard and rulers.
But that’s probably too judgmental.


After all there’s something self-referential and almost recursive about repeated applications of the square function. And it serves as the basis for Euclidean distance (and standard deviation formula) via the Pythagorean theorem.
How those two are connected is a mystery I still haven’t wrapped my head around. But a cool connection I have come to understand is that between:


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I guess first of all one has to appreciate that “parabola” shouldn’t necessarily have anything to do with x•x. Hopefully that’s become more obvious if you read the sections above where I point out that the target ℝ isn’t any more “vertical” than is the source ℝ.
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The inverse-square laws show up everywhere because our universe is 3-dimensional. The surface of a 3-dimensional ball (like an expanding wave of gravitons, or an expanding wave of photons, or an expanding wave of sound waves) is 2-dimensional, which means that whatever “force” or “energy” is “painted on” the surface, will drop off as the square rate (surface area) when the radius increases at a constant rate. Oh. Thanks, Universe, for being 3-dimensional.

What’s most amazing about the parabola—gravity connection is that it’s a metaphor that spans across both space and time. The curvature that looks like a-plane-figure-equidistant-to-a-line-and-a-point is curving in time.

In game theory the word “strategy” means a fully specified contingency plan. Whatever happens—be it a sequence of things, a conditional branching of their responses and my responses—∃ a contingency.
I can’t prove this, but I do feel that sometimes people talk about others as constants rather than response functions.
(A function is a ≥1-to-1 association from elements of a source domain to elements of a target codomain. I’ll owe ya a post on how this is not the most intuitive way to think about functions. Because it depends which domains you’re mapping from and to. Think for example about automorphisms—turning something over in your hand—versus measures—assigning a size to something.)
For example, extraversion vs introversion. This is one of the less disputatious dimensions of human variation from the MBTI. We can observe that some people (like me) gain more energy by being around people and feel like sh*te when they spend too much time alone, whereas others (like my best friend) replenish their reserves by being alone and drain them when they go out in public.
So we observe one datum about you—but sometimes a discussion (eg, an economics debate) wants to veer over counterfactual terrain—in which case we need a theory about how things might else have been.
I can think of other aspects of myself that are obviously responses to situational stimuli rather than innate constants.
Besides being motivation for me to learn more maths to see what comes out of this way of thinking about people when you layer abstract algebra over it, this view of people is a reminder to
Someone who piss me off may not be “a jerk”, it may not be about me whatever, s/he may be lag-responding to something from before I was there. Or s/he may not have adapted to a “nice guy” equilibrium of interacting with me. Who knows. I’m not seeing all of that person’s possibility, just a particular response to a particular situation.
On the other hand, if they really are acting wrong, it’s up to me to address the issue reasonably right away, rather than let my frustration passive-aggressively fester. Wait ten years for revenge and they’ll be a different person by then.
The final suggestion of people-as-functions is that there’s always something buried, something untapped—like part of a wavefunction that will never be measured, or a button on a machine that never gets pressed. You may see one version of yourself or someone else, but there’s more latent in you and in them—if you’re thrown into a war, a divorce, the Jazz Age, the Everglades, a hospice, a black-tie dinner, poverty, wealth, a band, a reality show about life under cruel premodern conditions—that may bring out another part of them.
UPDATE: peacemaker points out the similarity between people-as-response functions and the nature/nurture debate. I think this viewpoint subsumes both the nature and the nurture side, as well as free will.
UPDATE 2: As I think through this again, I feel quantum measurement really is a great metaphor for interacting with people. You only evoke one particular response-complex from a person on that particular time. And the way you evoke it perturbs the “objective” underlying thing. For example if yo’re introduced to someone in a flirtatious way versus in a business setting.
