  Model Predictive Control (MPC) is a popular optimization-based control
technique. MPC is usually formulated as sparse or dense Quadratic Programming
(QP). This paper reviews two well-known methods, namely, state condensing and
move blocking, and brings them together. Their combination results in
generalized QP that serves arbitrarily sparse (or dense) QP for MPC with move
blocking. The proposed QP can be solved by a specialized solver capable of
exploiting a sparsity structure of the problem. Numerical examples give inside
in computational and memory requirements.
