Active Set Block Barzilai-Borwein Method for Model Predictive Control

This study considers an active set algorithm for solving large-scale bound constrained optimization problems. We propose an active set block Barzilai-Borwein method and demonstrate its global convergence property. This method aims to separate decision variables from blocks. In numerous applications, such as model predictive control (MPC), problem variables often have a block structure. This studyformulated the application of active set methods to MPC using the penalty method. In our numerical experiments, we evaluated the proposed method’s effectiveness and showed its practical applicability to problems characterized by a block-structured variable configuration.