rtrdsvd

 RTRDSVD   Compute dominant SVD of a rectangular matrix

 This computes the dominant singular values and singular vectors of 
 a rectangular matrix A by optimizing the function 
   f(U,V) = trace(U'*A*V*N)
 on the manifold St(k,m) x St(k,n) using the Riemannian Trust-Region 
 with truncated CG inner solver.

 The manifold St(k,m) x St(k,n) is the product of two orthogonal Stiefel manifolds:
   St(k,n) = {X \in R^{n x k} such that X^T X = I_k}

 S = rtrdsvd(A,k) returns the dominant k singular values
 [U,S,V] = rtrdsvd(A,k) returns the dominant k singular vectors and values
 [U,S,V,stats] = rtrdsvd(A,k) returns in addition some statistics from the solver. 
 See RTR for info.

 rtrdsvd(A,k,params) allows the user to specify parameters that are passed 
 to the RTR solver.
   params.x0        - initial iterate: x.U orthonormal and x.V orthonormal
   params.Delta_bar - maximum trust-region radius
   params.Delta0    - initial trust-region radius
   params.epsilon   - Outer Convergence tolerance (absolute)

 See also rtr, rtreig, rtreig2, rtrflat