Is the Magnitude of Descent Directions on Manifolds Meaningful?

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In summary, the conversation discusses optimization on a manifold, specifically the NxN unitary matrices. The concept of "descent directions" on the manifold is explained, where a geodesic is considered to be a descent direction if its derivative along a curve is negative. The question is whether the magnitude of the derivative has any significance in determining the effectiveness of a descent direction. The answer is that the numbers may not be meaningful and only the sign is important, and to compare the effectiveness, the vectors should be normalized and the decent rate will vary based on distance from the starting point.
  • #1
Kreizhn
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Hey,

I'm trying to do some optimization on a manifold. In particular, the manifold is [itex] \mathfrak U(N) [/itex], the NxN unitary matrices.

Now currently, I'm looking at "descent directions" on the manifold. That is, let [itex] f: \mathfrak U(N) \to \mathbb R [/itex] be a function that we want to minimize, [itex] p \in \mathfrak U(N) [/itex] a point and [itex] X \in T_p \mathfrak U(n)[/itex] a point in the tangent manifold at p. If [itex] \gamma: \mathbb R\to \mathfrak U(N) [/itex] is a curve with [itex] \gamma(0) = p, \dot \gamma (0) = X [/itex] then the geodesic emanating from p in the direction X is [itex] \gamma_X(t) = \exp_p[tX] [/itex]. This geodesic is a descent direction if
[tex] \dot \gamma_X(0)f = \left.\frac d{dt} f(\gamma_X(t))\right|_{t=0} < 0 [/tex]

My question is: does the magnitude of [itex] \dot\gamma(0)f [/itex] mean anything? For example, say that for a fixed p and two different [itex] X_1,X_2 \in T_p\mathfrak U(N) [/itex] I get that
[tex] \dot \gamma_{X_1}(0) f = -85, \qquad \dot \gamma_{X_2}(0)f = -4 [/tex]
These are both descent directions, but is one a "better" descent direction than the other? That is, does [itex] X_1[/itex] result in a decrease in f faster than [itex] X_2 [/itex]? Or are the numbers fairly meaningless?

The above numbers are not far from actual values taken. The problem is that the direction of steepest descent (the negative of the Riemannian gradient) gives a value of -7. This would lead me to believe that the numbers are fairly useless, and only the sign is important. Any insight would be useful.
 
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  • #2
You are searching for "directions". To compare the effectiveness of choosing one direction rather than other you should first normalize your vectors [tex]X_1,X_2[/tex].
 
  • #3
Also you're only going to get a local decent rate - decent rate will change as you move away from p.
 

FAQ: Is the Magnitude of Descent Directions on Manifolds Meaningful?

1. What are descent directions on manifolds?

Descent directions on manifolds refer to the directions in which the function decreases on a given manifold. These directions are used in optimization problems to find the minimum value of a function on the manifold.

2. How are descent directions calculated on manifolds?

Descent directions on manifolds are calculated using the tangent space of the manifold and the gradient of the function. The gradient is projected onto the tangent space to find the direction in which the function decreases the fastest.

3. What is the significance of descent directions on manifolds?

Descent directions on manifolds are crucial in optimization problems on non-Euclidean spaces. They allow us to find the minimum value of a function on a curved surface, which cannot be achieved by traditional gradient descent methods on flat spaces.

4. How do descent directions on manifolds differ from Euclidean spaces?

In Euclidean spaces, descent directions are simply the opposite of the gradient of the function. However, on manifolds, the gradient needs to be projected onto the tangent space to find the descent direction, as the manifold is curved and the traditional gradient may not be a valid direction.

5. Can descent directions on manifolds be used in machine learning?

Yes, descent directions on manifolds have been successfully applied in machine learning for problems such as dimensionality reduction and data clustering. They are particularly useful for data sets with non-linear relationships, as they can find the optimal solution on the curved manifold instead of the flat Euclidean space.

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