Abstract

In the last decade, we published some splitting contraction methods for solving linearly constrained convex optimization. These methods are effectively applied to solving the large optimization problems arising from image processing, machine learning and other applied computation areas. In practice, the splitting contraction methods are closely related to our previous research work on the projection and contraction methods for monotone variational inequalities. This talk will give an overview on our research process. Our achievements show that only simple methods can be favored by the engineering community. Scholars in related fields say that our articles are easy to read. The only mathematical background required is the basic concepts of calculus and linear algebra.