# How do mathematicians think about high dimensional geometry?

Many ideas and algorithms come from imagining points on 2d and 3d spaces. Be it in function analysis, machine learning, pattern matching and many more.

How do mathematicians think about higher dimensions? Can intuitions about the meaning of dot-product, angles and lengths transfer from 2d geometry to a 100d?

If so, would it be enough to fully understand the higher dimesions, namely, could the same problem in 100d have properties\behaviours that are not seen in 2d\3d?

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Usually in algebraic geometry there's a "local-global" principle, where we study how a curve behaves locally and try to extend the results globally. –  Eugene Jun 13 '12 at 21:41
To think about $100$ dimensions, think of $n$-dimensions and set $n=100$. –  user17762 Jun 13 '12 at 21:42
Related: Intuitive crutches for higher dimensional thinking on MathOverflow. –  Rahul Jun 13 '12 at 21:44
Just to give you a very concrete example: two lines in the plane are either parallel or intersect in exactly one point. However, in three-dimensional space, one can have two lines that aren't parallel and have empty intersection: en.wikipedia.org/wiki/Skew_lines. –  talmid Jun 13 '12 at 22:08
Also, a volume ($n$-dimentional measure) of a $n$-dimentional ball as a function of $n$ is increasing upto 5d and then decrasing! –  dtldarek Jun 13 '12 at 22:20

It vastly depends on the objects you define. Indeed, when talking about vector spaces, we algebraically think about $\mathbb{R}^n$, build up intuition, and then set $n=100$.
However, when you start adding exotic objects, like knots, it becomes less "easy". For example, some knots in $\mathbb{R}^3$ are trivial loops in $\mathbb{R}^4$ (ie. the trefoil knot falls apart in 4D).
Then again, functions and their orthogonality are computed in $\mathbb{R}^{89270}$ just as they are in $\mathbb{R}$ - nothing strange going on there. It's only when you consider infinite-dimensional spaces that this becomes slightly unintuitive again.
So, in short, it completely depends on the objects you talk about. Most finite-dimensional vector spaces over some field $K$ are equal in almost all aspects. Adding more structure can make it much more difficult, and oftentimes all mathematicians have is algebra.