ABSTRACT

In this paper, an adaptation of the sequential quadratically constrained quadratic programming method is proposed to solve inequality constrained minimization. At each iteration, it solves a norm-relaxed quadratically constrained quadratic programming subproblem that uses an active set identification technique to reduce the scale and computational cost. By taking a valid line search, the iterates always get into the feasible set after a finite number of iterations, and followed by a suitable update rule for the penalty parameters. Under suitable conditions without the strict complementarity, our method has the global and superlinear convergence properties. In addition, numerical results are reported to demonstrate the efficiency of the proposed method.

摘要

在本文中，提出了一种改进的顺序二次约束二次规划方法来解决不等式约束最小化问题。在每次迭代中，它解决了一个范数松弛二次约束二次规划子问题，该子问题使用活动集识别技术来降低规模和计算成本。通过进行有效的线搜索，迭代总是在有限次数的迭代后进入可行集，并遵循惩罚参数的合适更新规则。在没有严格互补性的适当条件下，我们的方法具有全局和超线性收敛特性。此外，报告的数值结果证明了所提出方法的效率。