  We show that the {\em column sum optimization problem}, of finding a
$(0,1)$-matrix with prescribed row sums which minimizes the sum of evaluations
of given functions at its column sums, can be solved in polynomial time, either
when all functions are the same or when all row sums are bounded by any
constant. We conjecture that the more general {\em line sum optimization
problem}, of finding a matrix minimizing the sum of given functions evaluated
at its row sums and column sums, can also be solved in polynomial time.
