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’t )
subject to
norm(A1x(n)-b, Inf)<=t(1,1);
norm(A2x(n)-b, Inf)<=t(2,1);
norm(A3x(n)-b, Inf)<=t(3,1);
norm(A4x(n)-b, Inf)<=t(4,1);
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!