Question about the combination of lift and Product manifolds

[Zhao Xingyu]

I have an optimization problem with two optimization variables: {x∈Rn
 :−1≤xi≤1,∀i}and {Y∈Cn×m:∥Y∥F=1}.

Clearly, Y lies on the complex sphere manifold, while x can be optimized using the cubeslift operation. My question is, based on the cubeslift operation, can the Product manifolds be used to optimize both x and Y simultaneously? Could you provide relevant examples or literature for reference? Thank you very much!

[Nicolas Boumal]

Hello!

Exactly as you said: separately, we could optimize for Y on the complex sphere, and for x through a cubes lift. Currently, "products" are not supported for lifts (they were implemented fairly recently).

Since lifts are implemented as a change of variable, the most direct approach is probably to implement that change of variable manually, as follows.

Use productmanifold to define the product {z \in R^n} x {Y : ||Y|| = 1}, like this:

elems.z = euclideanfactory(n);

elems.Y = spherecomplexfactory(n, m);

manifold = productmanifold(elems);

Then define your problem structure with a manifold and a cost function, like so:

problem.M = manifold;

problem.cost = @mycostfunction;

function f = mycostfunction(X)

   % a point X on the product manifold is a structure with two fields, named z and Y (see "elems" above)

   z = X.z;

   Y = X.Y;

   x = sin(z); % this smooth change of variable ensures -1 <= x_i <= 1

   f = ...; % implement your function of x and Y, as you normally would

end

You likely also want to implement the gradient and maybe the Hessian. Here, it's important to note that you technically need to implement the gradient of g(z, Y) = f(sin(z), Y), where f(x, Y) is your original cost function and g is the cost function that Manopt actually optimizes. The gradient should also be a structure with two fields: one for the gradient with respect to z, and one wrt Y. You can implement problem.egrad for example (as opposed to problem.grad, without "e") if you just want to provide the Euclidean gradient. Then, Manopt will automatically adapt this to compute the Riemannian gradient.

When you run an optimization algorithm as

X = trustregions(problem);

your solution is 

   z = X.z;

   Y = X.Y;

I hope this helps.

Best,

Nicolas