Hi all！

I have a problem just like the sum-of-norms problem described in chapter 6.4: Robust approximation from the book *Convex Optimation*.

,

Just like the above but use the Chebyshev norm and write it in Matlab as follows:

p=0.2.*ones(5,1);
cvx_begin quiet
variable t(5) nonnegative
variable x(n)
minimize ( p’*x(n)-b, Inf)<=t(2,1);

*t ) subject to norm(A1*x(n)-b, Inf)<=t(1,1); norm(A2

norm(A3

*x(n)-b, Inf)<=t(3,1);*

norm(A4x(n)-b, Inf)<=t(4,1);

norm(A4

norm(A5*x(n)-b, Inf)<=t(5,1);

cvx_end

A1, A2, A3, A4, A5 are m plus n matrixes.

Run these codes and I get the error:

Error using cvx/plus (line 45)

Matrix dimensions must agree.

Error in cvx/minus (line 21)

z = plus( x, y, true, cheat );

Error in test_CVX(line 266)

norm(A1*x(n)-b, Inf)<=t(1,1);

Why？Any help would be appreciated! Thanks！