AN INTERIOR-POINT ALGORITHM FOR SIMPLICIAL CONE CONSTRAINED CONVEX QUADRATIC OPTIMIZATION

In this paper, we are concerned with the numerical solution of simplicial cone constrained convex quadratic optimization (SCQO) problems. A reformulation K.K.T optimality conditions SCQOs as an equivalent linear complementarity problem $\mathcal{P}$-matrix ($\mathcal{P}$-LCP) is considered. Then, a feasible full-Newton step interior-point algorithm (IPA) applied for solving SCQO via $\mathcal{P}$-LCP. For completeness study, prove that proposed well-defined and converges locally to optimal SCQOs. Moreover, obtain currently best well-known iteration bound short-update method, namely,$ \mathcal{O}(\sqrt{n}\log\frac{n}{\epsilon })$. Finally, present various set results show its efficiency.