Hello, guys.

A very intractable problem confused me. I can’t deal with that. Please help me .I’m striggle with it for one day.

I need kind guys to teach me how to do.

Here is the problem：

I just want to sovle a non-limited minimal optimal problem with CVX.

In the objective function, there is a term of row-based l2-norm of matrix Z.

n times n Matrix Z is a variable and is typoed as variables Z(n,n)

where, p is p-norm, z_i is the rows of the matrix Z

Thanks very much for your kind help.

# The CVX problem of expressing row-based matrix l2-normalization

**icesky**(Jianhai Zhang) #1

**Mark_L_Stone**(Mark L. Stone) #2

`sum(norms(Z,p,2))`

does what you want.

help norms

norms Computation of multiple vector norms.

norms( X ) provides a means to compute the norms of multiple vectors

packed into a matrix or N-D array. This is useful for performing

max-of-norms or sum-of-norms calculations.`All of the vector norms, including the false "-inf" norm, supported by NORM() have been implemented in the norms() command. norms(X,P) = sum(abs(X).^P).^(1/P) norms(X) = norms(X,2). norms(X,inf) = max(abs(X)). norms(X,-inf) = min(abs(X)). If X is a vector, these computations are completely identical to their NORM equivalents. If X is a matrix, a row vector is returned of the norms of each column of X. If X is an N-D matrix, the norms are computed along the first non-singleton dimension. norms( X, [], DIM ) or norms( X, 2, DIM ) computes Euclidean norms along the dimension DIM. norms( X, P, DIM ) computes its norms along the dimension DIM. Disciplined convex programming information: norms is convex, except when P<1, so an error will result if these non-convex "norms" are used within CVX expressions. norms is nonmonotonic, so its input must be affine.`