I am using cvx to solve linear programs with constraints of the form $Ax=b,x\ge0$. However the matrix $A$ is rank deficient and cvx returns a warning and finally displays status as 'Infeasible'. Rank deficient systems can have a solution and my guess is that my system does have a solution. Is there a way to make cvx solve this system without making the matrix full rank?
Solving rank deficient systems with cvx
linear programming;cvx
It's known that CVX itself, and to an extent the solvers it uses, have issues with rank deficiency in the equation matrix. (Hence the warnings.) But what is your aversion to doing some sort of LU factorization here? Also, have you tried all of the solvers, or just one?Another approach is to solve a model that is guaranteed to be feasible. For instance:cvx_begin    variables x(n)    minimize(norm(A*x-b))    x >= 0cvx_endIf your original problem is feasible, this should have an optimal value that is near zero (to within roundoff error). If it has a non-negligible, positive optimal value, then your original model was infeasible.