§ Repulsive curves
§ Gradient depends on norm
- consider a function f:Rn→R and an energy e:(Rn→R)→R.
- We want to optimize df/dt=−∇e(f).
- however, what even is ∇?
- Recall that de is the differential which at a point f on the space Rn→R, in a tangent direction u∈Rn→R, computes de∣f(u)≡limϵ→0(e(f+ϵu)−e(f))/ϵ.
- Now the gradient is given by ⟨grad(e),u⟩X≡de(u). So the gradient is the unique vector such that the inner product with the gradient produces the value of the contangent evaluated in that direction.
- Said differently, ⟨nabla(e),−⟩=de(−). This is a Reisez like representation theorem.
- Note that asking for an inner product means we need a hilbert space.
- One choice of inner product is given by L2, where ⟨u,v⟩L2≡∫⟨u(x),v(x)⟩dx.
- More generaly, we can use a Sobolev space, where we define the inner product given by ⟨u,v⟩H1≡⟨∇u,∇v⟩L2, which can also be written as ⟨∇u,v⟩L2.
- Similarly, for the sobolev space H2, we would use ⟨u,v⟩H2≡⟨∇u,∇v⟩L2. which is equal to ⟨∇2u,v⟩L2.
- In general, we can write our inner product as something like ⟨Au,v⟩L2.
§ Solving heat equation with finite differences
- Solving df/dt=∇f=d2f/dx2.
If we try to solve this equation using, say, explicit finite differences with grid spacing h, we will need a time step of size O(h 2) to remain stable—significantly slowing down computation as the grid is refined
§ Different norm is good for different situations
§ Tangent point energy
- Key intuition: want energy that is small for points that are "close by" in terms of t on the knot, want energy that repels points on the knot that are far away in terms of t by close by in terms of f(t).
- TODO