subject to the constraint that square of Frobenius norm of Ds matrix has to be less than or equal to 1.

Currently I am using CVXPY library to solve the objective function. My code sample looks like

```
import cvxpy as cp
import numpy as np
np.random.seed(1)
Xs = np.random.randn(100,4096)
Ys = np.random.randn(100,300)
# Define and solve the CVXPY problem.
Ds = cp.Variable(shape=(300,4096))
lamda1 = 1
obj = cp.Minimize(cp.square(cp.norm(Xs - (Ys*Ds),'fro'))
+ lamda1*cp.norm(Ds,'nuc')) constraints = [cp.square(cp.norm(Ds,'fro')) <= 1]
prob = cp.Problem(obj, constraints)
prob.solve(solver=cp.SCS, verbose=True)
```

The console gives an error that

Solver ‘SCS’ failed. Try another solver or solve with verbose=True for more information. Try recentering the problem data around 0 and rescaling to reduce the dynamic range.

I event tried to experiment with different solvers like cp.ECOS but they do not optimize the function.

Any suggestions ?