  Inspired by protein folding, we introduce funnels into the complex cost
function landscapes of two processes, the tuning of networks, and the jamming
of ideal spheres. In both processes, geometrical frustration plays a role --
tuning pressure differences between pairs of target nodes far from the source
in a flow network impedes tuning of nearby pairs more than the reverse process,
while unjamming the system in one region can make it more difficult to unjam
elsewhere. By modifying the cost functions to control the order in which
functions are tuned or regions unjam, we smooth out local minima while leaving
global minima unaffected, significantly increasing the success rate of reaching
global minima.
