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A Reduced-Space Interior-Point Quasi-Sequential Approach to Nonlinear Optimization of Large-Scale Dynamic Systems

机译:大型动态系统非线性优化的空间缩减内点拟序法

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We propose a reduced-space interior-point approach to nonlinear optimization problems with general inequality constraints. It is an extension of the quasi-sequential approach to dynamic optimization of large-scale systems. Inequality constraints are formed by adding slack variables to an equality constrained barrier (interior-point) problem which is solved by a range space step and a null space step in every iteration. Mathematical derivations and computation schemes are presented. We take a highly nonlinear parameter estimation problem as an example to demonstrate the effectiveness of this approach. The result is compared with the full space approach in terms of overall CPU time and number of iterations.
机译:我们提出了一种具有一般不等式约束的非线性优化问题的缩减空间内点方法。它是准顺序方法对大型系统动态优化的扩展。不等式约束是通过将松弛变量添加到等式约束障碍(内部点)问题而形成的,该问题在每次迭代中均通过范围空间步长和空值空间步长来解决。提出了数学推导和计算方案。我们以一个高度非线性的参数估计问题为例来证明这种方法的有效性。将结果与全空间方法在总体CPU时间和迭代次数方面进行了比较。

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