Enhancing Semidefinite Relaxation for Quadratically Constrained Quadratic Programming via Penalty Methods
Enhancing Semidefinite Relaxation for Quadratically Constrained Quadratic Programming via Penalty Methods is a scholarly work, published in 2018 in ''Journal of Optimization Theory and Applications''. The main subjects of the publication include second-order cone programming, quadratic programming, convex optimization, mathematical optimization, Quadratic growth, Semidefinite embedding, semidefinite programming, bisection method, regular polygon, compressed sensing, applied mathematics, mathematics, quadratically constrained quadratic program, combinatorial optimization, quadratic equation, and relaxation. The authors then introduce a special penalty method for quadratically constrained linear programming based on its semidefinite relaxation, resulting in the so-called conditionally quasi-convex relaxation.