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What are imaginary numbers?

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Re: What are imaginary numbers?

#51
post #34

Can someone please also do this for: 1. Matrices, especially matrix multiplication. Unlike matrix addition, multiplication is defined in a very weird way. I think I understand where it is coming from -- defining it that way allows representing and solving linear equations. More insights, however, would help. 2. Dot and cross products. E.g., the magnitude of dot product in 3D is a.b.cos(theta), while for cross product…

Here's a fantastic article explaining scalars, matrices and tensors: http://www.grc.nasa.gov/WWW/k-12/Numbers/Math/documents/Tens...

Explanation of why dot and cross products are defined that way: http://physics.stackexchange.com/questions/14082/what-is-the...

Re: What are imaginary numbers?

#52
post #49

A complex "number" (don't think of it as of a number! think of it like you would think of a vector, group, ring or any other abstract structure) is just an ordered pair of real numbers that behaves in a certain predefined way when being added to another complex number or multiplied by it. For an introduction, to avoid unnecessary confusion, it is best to write such "numbers" as ordered pairs using the notation: (a,b)…

"A complex "number" (don't think of it as of a number! ..."

Excellent point! Another example of misleading mathematical terminology is "random variable", which are not random or variable but instead are well-defined mappings.

Re: What are imaginary numbers?

#53
post #34

Can someone please also do this for: 1. Matrices, especially matrix multiplication. Unlike matrix addition, multiplication is defined in a very weird way. I think I understand where it is coming from -- defining it that way allows representing and solving linear equations. More insights, however, would help. 2. Dot and cross products. E.g., the magnitude of dot product in 3D is a.b.cos(theta), while for cross product…

I'll try to give some short pointers. 1. Matrices represent linear functions between finite-dimensional vector spaces. That is, if you have a function f from V to W that satisfies f(ax + by) = af(x) + bf(y), then there is a matrix A such that f(x) = Ax, and vice versa. Once you understand that, try to figure out what happens to those matrices when you compose functions. In other words, when you define h(x) = g(f(x))…

Here is what I am confused about with regards to diagonalisation:

Start with binary non-negative integers: 000 001 010 011 100 101 ... (goes to infinity)

This set now includes all possible bit strings of infinite length since the way these are iteratively generated includes all possibilities.

This is also an enumerable set by definition.

Let's now reverse the bits and put them after a decimal. These are just real numbers now going from zero to one. (This step is actually unnecessary I think.) .000 .100 .010 .110 .001 .101 .011 ...

This must be enumerable set too.

Using diagonalisation argument, .111111 is never to be found in this set. This is exactly where I am stuck. This number comes into the set from flipping of infinity in the original set, which includes all possible stings of infinite length.

I immediately read into your message on treating these as bit strings instead of real numbers. I still am stuck though. (Do you also see a connection to 0.99999... by the way?)

Re: What are imaginary numbers?

#54
post #4

Here is an even better discussion on the same topic, and the HN thread from last year: http://betterexplained.com/articles/a-visual-intuitive-guide... https://news.ycombinator.com/item?id=2712575 One great conclusion from this approach is how intuitive it becomes to understand the square root of i . I always thought you'd need another dimension to describe that, and another dimension for the square root of that unit,…

One more question; what is the "i"th root of i? Please try to use the angle metaphor :)

This is of course much trickier because to define z^w for complex numbers requires that one choose a complex logarithmic function. There are infinitely many choices for this. Everyone has decided to use the principle logarithm but it could be consistently defined using any branch cut.

Re: What are imaginary numbers?

#55
post #34

Can someone please also do this for: 1. Matrices, especially matrix multiplication. Unlike matrix addition, multiplication is defined in a very weird way. I think I understand where it is coming from -- defining it that way allows representing and solving linear equations. More insights, however, would help. 2. Dot and cross products. E.g., the magnitude of dot product in 3D is a.b.cos(theta), while for cross product…

I don't have time now to provide complete answers of the type you want, but I can give you a little insight.

  > Matrices, especially matrix multiplication.
Consider a transformation of space, specifically a shear, or a rotation, or an expansion (or contraction). All of these leave the origin unmoved, and a line will always become a line. They are what we call linear transformations.

So given a point (x,y,z) in 3D space, the new values of x, y, and z are obtained by equations like a * x + b * y + c * z. If you then chase through how to combine two of these transformations, one after the other, the definitions of matrix multiplication drop out.

This, by the way, also explains why you can't always divide by a matrix. Sometimes the transformation collapses the space into a lower dimensional space, and that can't be undone.

  > Dot and cross products.
For unit vectors u and v, the dot product tells you how much of u points in the direction of v. Then we want it to be true that doubling the vector length doubles the size of the product. Then the result you ask about - the magnitude of dot product in 3D is a.b.cos(theta) - automatically follows.

With the cross product, that can be defined as the area of a parallelogram that has the vectors as the side. Again, thinking about that interpretation shows how we get the answer you ask about.

  > How is a set and "belongs-to" operator defined?
Set theory is often regarded as axiomatic, and so you can go back to the axiomatic definitions. We have a universe of discourse that has a bunch of atomic things. A "set" is then a collection of things, and then each set becomes a new thing in our universe. Thus sets can contain other sets.

So a set is defined by what things are in it, so the "belongs-to" concept is fundamental to how the set in question is specified.

  > Why is 0.9999... considered to be "equal" to 1.
People trip up on this in part because they think somehow that something is moving, that 0.999... "approaches" something. But no, when we write "0.999..." we have a representation of a point on the real line. Deciding what it is that you are defining requires that you be clear and detailed about what you think 0.999... means.

So what does it mean? We drop back to saying that it's the limit of the sequence 0.9, 0.99, 0.999, 0.9999, ... so we need to ask what real number can possibly be that limit? Let's call the limit L, and ask what it might be.

Well, clearly:

   L > 0.9
   L > 0.99
   L > 0.999
   L > 0.9999
... and so on.

Now, for every number X that's less than 1, there'll be something in that sequence that will be bigger than X. That means that the limit can't be less than 1. But equally, none of those numbers is bigger than 1, so the limit can't be bigger than 1.

In fact, the smallest upper bound of { 0.9, 0.99, 0.999, 0.9999, ... } is 1.

And then, why should it not be 1? We have different representations of points elsewhere. 2/3 is also 4/6 and 6/9, and sqrt(8) is the same as 2 * sqrt(2). These are simply different ways of specifying the same place on the real line. Likewise, writing 0.9999... is just a different representation of the point more often written as 1.

You said:

  > I understand them to be equal "under the limit",
  > but not without.
You tell me what you mean by 0,9999... and then I'll answer your question in more detail.

  > This seems to be in my way of understanding Cantor's infinities.
Well, there are two different types of infinities based on set theory, but you're probably asking about cardinal infinities, the infinity of counting stuff - "How many things are in this set."

So here, I have a sack with a collection of cubes, and they're numbered from 1 onwards. Call that collection A.

Now let's take a cube and divide one face into two sections. Flip a coin, and write the result in one of the sections. in the other section, divide into two, flip a coin, write the result in one section, and then lather, rinse, repeat. We can fit infinitely many coin flips onto the cube in this way. In sack B put a cube with every possible result of this process.

I claim that the cubes from sack A and sack B cannot be paired off with nothing left over. I won't prove that here, I'm now out of time.

So, ask questions.

Re: What are imaginary numbers?

#56
post #34

Can someone please also do this for: 1. Matrices, especially matrix multiplication. Unlike matrix addition, multiplication is defined in a very weird way. I think I understand where it is coming from -- defining it that way allows representing and solving linear equations. More insights, however, would help. 2. Dot and cross products. E.g., the magnitude of dot product in 3D is a.b.cos(theta), while for cross product…

I can try:

1. Matrix multiplication is indeed defined oddly. To understand why, it can help to think of matrices as "linear transformations". For example, the matrix

| 0 -1 |

| 1 0 |

corresponds to counterclockwise rotation of vectors by 90 degrees (go ahead, try multiplying the vector by this matrix - the answer should be ). Let's call this matrix A. Also, the matrix

|-1 0 |

| 0 1 |

corresponds to reflection over the y-axis (call this matrix B). If we begin with a vector v = , then rotate twice by 90 degrees, then reflect over the y-axis, we should get the vector (it helps to draw a picture, but I am limited in these comments). We can figure this out by repeatedly multiplying by matrices: Av = , A(Av) = , and B(A(Av)) = (you may want to multiply these out to check, then check by evaluating the operations geometrically).

But wait - there is also a matrix which represents this transformation (that is, the transformation which is rotation counterclockwise by 90 degrees twice then reflection over the y axis). It's the matrix

| 1 0 |

| 0 -1 |

which we'll call C (go ahead and check that this matrix works the way that I just said). Since multiplying a vector by C is the same as multiplying the vector by A then A then B, we have mathematically that

Cv = B(A(Av))

Now, it sure would be nice if there was a way to "multiply" matrices together so that the product B(AA) = C. In abstract terms, we want multiplication of matrices to correspond to composition of functions, since we can use matrices to represent functions. If we define multiplication of matrices in the usual "weird" way, then this property holds!

This lets us examine composing functions by multiplying matrices, which is a very powerful tool indeed. Another interesting thing to notice is that this explains why multiplication of matrices is not commutative (that is, order matters). If we rotate counterclockwise by 90 degrees, then reflect over the y axis, that's different than reflecting over the y axis then rotating counterclockwise by 90 degrees. So BA is not equal to AB.

2. I'm not sure that I understand your question here.

3. This question is beyond my ability to answer, so I'll leave it to someone else.

4. On the one hand, there are tricks to see this (e.g. 0.333... = 1/3, so 0.9999... = 3 * 0.3333... = 3 * (1/3) = 1), but I expect that you have seen them and been unsatisfied by them (I don't believe that these are satisfying explanations, personally, because I have simply replaced the unknown fact 0.999... = 1 with the equally unfounded fact 0.333.... = (1/3)).

In order to really understand what's going on, we have to understand what we really mean by 0.999... What is meant by 0.9? This is equal to 9/10. How about 0.99? This is 9/10 + 9/100. 0.999 = 9/10 + 9/100 + 9/1000. Following this reasoning, 0.999... is equal to the infinite sum 9/10 + 9/100 + 9/1000 + ... + 9/10^k + ...

Since there are infinitely many 9s in the decimal, we must add an infinite number of fractions of the form 9/10^k.

We've opened up a can of worms here - how do we add up infinitely many things? Also, if we add up infinitely many things, how can the result possibly be finite?

A Calculus II course will answer these questions rigorously, but let me attempt to make an explanation here.

Think of it as a game of wits. You start. Choose a number less than 1 - any number at all, just so long as it is less than 1. I'll try to find some number k such that 0.9999...999 (with k 9s in it) is closer to 1 than your number.

Suppose that you pick 0.97. That's pretty close to 1, only 0.03 away. But 0.01 You try again, this time picking 0.999878787. That's much closer to 1, but still 0.000121213. Since 0.0001 In fact, let's suppose that you pick the number x. Since x is less than 1, 1 - x is some positive (but very small) number. No matter how small it is, though, I can find some k such that 1/(10^k) is even smaller (this fact is called the Archimedean principle, and it is one of the fundamental theorems of the real numbers). Another way to think about it is that 1/(1-x) may be very big, but I can find some k such that 10^k is even bigger. But then 0.999...99 (with k 9s) is going to be closer to 1 than your number, and I'll win again.

Now, what does this mean? Since 0.999... is bigger than 0.999...99 (with any finite number of 9s), it is bigger than every number which is less than 1 (by the game we just played). The smallest number which is bigger than every number less than 1 is just 1. Anything smaller just won't do (since there would be numbers between it and 1). This shows that 0.999... is at least 1.

I hope that helps. It isn't as elegant as the featured answer, but maybe it can be a little bit helpful to you.

Re: What are imaginary numbers?

#57
post #48

Earlier quoted context omitted.

I'll try to give some short pointers. 1. Matrices represent linear functions between finite-dimensional vector spaces. That is, if you have a function f from V to W that satisfies f(ax + by) = af(x) + bf(y), then there is a matrix A such that f(x) = Ax, and vice versa. Once you understand that, try to figure out what happens to those matrices when you compose functions. In other words, when you define h(x) = g(f(x))…

Thanks!! >> then you have to talk about determinants instead of angles I certainly never heard this before. (I know how to calculate determinants, but never quite developed intuition around them.) Can you please say some more on this? :-)

Take a matrix M. This is a linear transformation. Look at what happens to the unit cube, and ask what volume the result has?

The answer is the determinant of the matrix.

Re: What are imaginary numbers?

#59
post #40

Earlier quoted context omitted.

One more question; what is the "i"th root of i? Please try to use the angle metaphor :)

Sure! You might want to check out http://betterexplained.com/articles/intuitive-understanding-... I like this question because it really works your intuition. The basics: x^y means "grow at x, for y units of time". I see "2^3" as "grow at 2x for 3 units of time". Having a base of i means your "growth" is a rotation at 90 degrees, no scaling. So i^(1/2) means a 45 degree rotation, i^3 means a 270 rotation, etc. Raisin…

Note that 2575.97 also has the property that raised to the ith power gives i. There are infinitely many such numbers.

Re: What are imaginary numbers?

#60
post #53

Earlier quoted context omitted.

I'll try to give some short pointers. 1. Matrices represent linear functions between finite-dimensional vector spaces. That is, if you have a function f from V to W that satisfies f(ax + by) = af(x) + bf(y), then there is a matrix A such that f(x) = Ax, and vice versa. Once you understand that, try to figure out what happens to those matrices when you compose functions. In other words, when you define h(x) = g(f(x))…

Here is what I am confused about with regards to diagonalisation: Start with binary non-negative integers: 000 001 010 011 100 101 ... (goes to infinity) This set now includes all possible bit strings of infinite length since the way these are iteratively generated includes all possibilities. This is also an enumerable set by definition. Let's now reverse the bits and put them after a decimal. These are just real num…

  > Start with binary non-negative integers:
  > 000 001 010 011 100 101 ... (goes to infinity)

  > This set now includes all possible bit strings of
  > infinite length
No, it only contains the strings of finite length. There are infinitely many of them, but each one stops after a while. In particular, then n^th one only has log2(n) places before it then becomes all 0s.

  > This is also an enumerable set by definition.
Yes.

  > Let's now reverse the bits and put them after a decimal.
  > These are just real numbers now going from zero to one.
  > ... .000 .100 .010 .110 .001 .101 .011 ...
But not all of them, since these are only those numbers that have a finite number of 1's in them.

  > This must be enumerable set too.
Yes it is.

  > Using diagonalisation argument, .111111 is never to be
  > found in this set.
Irrelevant.

  > This is exactly where I am stuck. This number comes
  > into the set from flipping of infinity in the original set,
"Infinity" was never in your original set. And if it was, it wouldn't be produced by the diagonalisation argument.

  > which includes all possible strings of infinite length.
No it doesn't.
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