ABSTRACT
When we minimize a real-valued function f(x), constraints on x often take the form of inclusion in certain convex sets. These sets may be related to the measured data, or incorporate other aspects of x known a priori. There are several related problems that then arise. Iterative algorithms based on orthogonal projection onto convex sets are then employed to solve these problems. Such constraints can often be formulated as requiring that the desired x lie within the intersection C of a finite collection {C1, ..., CI} of convex sets.
