Bias Versus Non-Convexity in Compressed Sensing
Cardinality and rank functions are ideal ways of regularizing under-determined linear systems, but optimization of the resulting formulations is made difficult since both these penalties are non-convex and discontinuous. The most common remedy is to instead use the ℓ1- and nuclear norms. While these are convex and can therefore be reliably optimized, they suffer from a shrinking bias that degrades