Aug 3, 2017 · This paper proposes a new algorithm for solving convex quadratic programs (QP) subject to linear inequality and equality constraints.

Jan 26, 2018 · Abstract—This paper proposes a new algorithm for solving con- vex quadratic programs (QP) subject to linear inequality and equal-.

This paper proposes a new algorithm for solving convex Quadratic Programs (QP) subject to linear inequality and equality constraints.

The method extends an approach recently proposed by the author for solving strictly convex QP's using nonnegative least squares, by making it numerically ...

Bibliographic details on A Numerically Stable Solver for Positive Semidefinite Quadratic Programs Based on Nonnegative Least Squares.

Jun 6, 2017 · Quadratic programs with rank deficient positive semidefinite matrices ... under convex conditions xi≥0 and yHx=0, where b,y some fixed vectors.

Apr 10, 2020 · This is a convex optimization problem which can be easily formulated, and then numerically solved via a convex optimization tool, such as CVX, ...

The algorithm couples a branch and bound (B&B) scheme with a recently proposed numerically robust Quadratic Programming (QP) solver based on nonnegative least ...

This technical note proposes an active set method based on nonnegative least squares (NNLS) to solve strictly convex quadratic programming (QP) problems, ...

Sep 5, 2018 · now i define a semidefinite matrix G=P^T*P. therefore,G >=0, and there are lots of zero in G(this doesn't hurt, because it is linear constraints) ...