Quadratic program solver for MPC using variable ordering
A system and approach for storing factors in a quadratic programming solver of an embedded model predictive control platform. The solver may be connected to an optimization model which may be connected to a factorization module. The factorization module may incorporate a memory containing saved factors that may be connected to a factor search mechanism to find a nearest stored factor in the memory. A factor update unit may be connected to the factor search mechanism to obtain the nearest stored factor to perform a factor update. The factorization module may provide variable ordering to reduce a number of factors that need to be stored to permit the factors to be updated at zero floating point operations per unit of time.
1. A process for solving a quadratic programming issue, comprising:
an off-line portion; and
an on-line portion; and wherein:
the off-line portion comprises:
finding an ordering of variables at each combination of a working set;
selecting and computing a set of factors for a model predictive control (MPC) quadratic programming (QP) issue; and
storing a selected subset of the set of factors in a memory; and
the on-line portion comprises:
initializing an on-line process;
building a matrix on a current set of constraints;
finding a nearest stored factor from the memory;
performing a factor update with the nearest stored factor;
solving the matrix; and
updating a solution for a QP issue.
2. The process of claim 1 , further comprising updating a working set of active constraints.
3. The process of claim 2 , wherein the matrix is a Karush-Kuhn-Tucker (KKT) matrix.
4. The process of claim 2 , further comprising reiterating the on-line portion.
5. The process of claim 2 , further comprising terminating the on-line portion.