An ADMM algorithm for solving a proximal bound-constrained quadratic program
This is an incremental improvement for optimization researchers and practitioners dealing with constrained quadratic programming.
The paper tackles the problem of efficiently computing a proximal operator for bound-constrained quadratic programs by proposing an ADMM-based algorithm, which is particularly effective for collections of proximal operators with shared quadratic forms or relaxations of binary quadratic problems.
We consider a proximal operator given by a quadratic function subject to bound constraints and give an optimization algorithm using the alternating direction method of multipliers (ADMM). The algorithm is particularly efficient to solve a collection of proximal operators that share the same quadratic form, or if the quadratic program is the relaxation of a binary quadratic problem.