Sparse Linear-Quadratic Feedback Design Using Affine Approximation

We consider a class of ℓ0-regularized linear-quadratic (LQ) optimal control problems. This class of problems is obtained by augmenting a penalizing sparsity measure to the cost objective of the standard linear-quadratic regulator (LQR) problem in order to promote sparsity pattern of the state feedback controller. This class of problems is generally NP hard and computationally intractable. First, we apply a ℓ1-relaxation and consider the ℓ1-regularized LQ version of this class of problems, which is still nonconvex. Then, we convexify the resulting ℓ1-regularized LQ problem by applying affine approximation techniques. An iterative algorithm is proposed to solve the ℓ1-regularized LQ problem using a series of convexified ℓ1-regularized LQ problems. By means of several numerical experiments, we show that our proposed algorithm is comparable to the existing algorithms in the literature, and in some cases it even returns solutions with superior performance and sparsity pattern.