  We consider minimizing a smooth function subject to a summation constraint
over its variables. By exploiting a connection between the greedy 2-coordinate
update for this problem and equality-constrained steepest descent in the
1-norm, we give a convergence rate for greedy selection under a proximal
Polyak-Lojasiewicz assumption that is faster than random selection and
independent of the problem dimension $n$. We then consider minimizing with both
a summation constraint and bound constraints, as arises in the support vector
machine dual problem. Existing greedy rules for this setting either guarantee
trivial progress only or require $O(n^2)$ time to compute. We show that bound-
and summation-constrained steepest descent in the L1-norm guarantees more
progress per iteration than previous rules and can be computed in only $O(n
\log n)$ time.
