Maybe us engineering types are hardwired this way. I suspect, however, that non-engineering types aren't wired this way due to poor parenting, weak schools, and fundamentally broken reward and punishment systems.
Maybe us engineering types are hardwired this way. I suspect, however, that non-engineering types aren't wired this way due to poor parenting, weak schools, and fundamentally broken reward and punishment systems.
Me too. That's why the wheels came off my math skills when I got to differential equations. Everything up to that point I'd been able to form a picture of in my head. For example, calculus is just Newtonian mechanics, so that's got a clear analog in the real world.
But Diff EQ was just too abstract. I was eventually able to pass it with the help of a couple friends pumping me full of formulas and procedures, but I never really understood, and so I never could really apply it on my own.
So let's say we have a vector which is represented by the matrix A of any dimension (any # of columns, each column a vector). If we multiply this matrix A with an arbitrary unit vector (considering just the direction in the given dimensions) B, then we'll have a certain set of possible resulting vectors (A X B). The eigenvectors are whenever the same unit vector B lines up in direction with (A X B)--where both vectors are dependent on B, and the eigenvalues are the corresponding scalars which must multiply the unit eigenvector to match the resulting A X B vector. The eigenvector does not have to be a unit vector, but since any multiple of the eigenvector can still be a valid eigenvector, you probably just want the unit vector or at least the lowest whole number reduction. If you reduce the eigenvector, the eigenvalue must be changed too.
In equation form: Ax=Ωx Where A is a matrix you are trying to transform, and x is the possible eigenvector, and Ω is the eigenvalue.
In graphical form (this was what really did the trick): http://ocw.mit.edu/OcwWeb/Mathematics/18-06Spring-2005/Tools... (Demo #1)
And the matrices are simply a different form of a set of equations while still preserving their relative properties. The matrix form allows you to easily manipulate the original set of data with matrix operations. I guess the matrix can also just represent a set of column vectors (which are just a direction in a space with a magnitude) too.
Let me know if I got anything wrong.
(btw: I love that professor today. He was just underhanded enough to do what we all needed.)
I'm utterly convinced that pure math needs to be taught at a high level for anyone in advanced science/engineering. It hurts for a while, and then you look back and everything is clear, simple, and bright shining.
I've also heard this just keeps happening if you keep on with math.
So, taking a differential equations class by itself can make it seem abstract and with limited connection to the physical world.
I'm chiming in as well because that is exactly how my mind works. I can remember very little trivia or material I am fleetingly familiar with. However, I do extremely well when applying concepts, and later I can remember intricacies extremely well.
I also have great difficulty presenting material that I don't fully understand, yet when I understand it I am a great public speaker.
That third party needn't even exist, once you get the hang of learning new and lesser-understood stuff that way.
You don't really understand something until you can explain it in clear terms to someone without any knowledge of the subject.
There's evidence that you're right about the correlation with upbringing. Someone did a study on elementary school children, teaching them basic one-digit multiplication, and the concepts of place value, and then had them invent their own algorithms for long multiplication rather than rote-memorize the standard one. The methods they invented were invariably less compact and required more work than the standard method, but the students also performed well above average on standardized tests for multiplication.
I suspect that the high-performing individuals in the "softer" disciplines like literary analysis also function in the same way, just on fluffier concepts and less rigorous abstractions.
Such scientific close-mindedness is cargo-cult science, my friend... I believe Feynman spoke on that one too.
(+) In my head, not, say, out loud while waiting for the bus.