Goran Banjac

TL;DR: This work presents a general-purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix at almost every iteration.

TL;DR: OSQP as discussed by the authors is a general-purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix at almost every iteration.

TL;DR: OSQP as mentioned in this paper is a general purpose solver for quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix in each iteration.

TL;DR: This work shows that in the limit the iterates of the alternating direction method of multipliers either satisfy a set of first-order optimality conditions or produce a certificate of either primal or dual infeasibility for optimization problems with linear or quadratic objective functions and conic constraints.

TL;DR: A code generation software package that accepts a parametric description of a quadratic program (QP) as input and generates tailored C code that compiles into a fast and reliable optimization solver for the QP that can run on embedded platforms is introduced.