Accelerated Algorithms for a Class of Optimization Problems with
  Equality and Box Constraints
Anjali Parashar Priyank Srivastava Anuradha M. Annaswamy
Convex optimization with equality and inequality constraints is a ubiquitous
problem in several optimization and control problems in large-scale systems.
Recently there has been a lot of interest in establishing accelerated
convergence of the loss function. A class of high-order tuners was recently
proposed in an effort to lead to accelerated convergence for the case when no
constraints are present. In this paper, we propose a new high-order tuner that
can accommodate the presence of equality constraints. In order to accommodate
the underlying box constraints, time-varying gains are introduced in the
high-order tuner which leverage convexity and ensure anytime feasibility of the
constraints. Numerical examples are provided to support the theoretical
derivations.