pySLEQP : A Sequential Linear Quadratic Programming Method Implemented in Python
pySLEQP : A Sequential Linear Quadratic Programming Method Implemented in Python
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pySLEQP：用Python实现的顺序线性二次规划方法
DOI：
10.1007/978-3-319-67168-0_9
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
H. G. Bock
中科院分区：
文献类型：
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作者：
F. Lenders;C. Kirches;H. G. Bock
我们提出了用于解决非线性规划问题的顺序线性等式约束二次规划 (SLEQP) 方法的原型实现。与 SQP 活动集方法类似，SLEQP 方法是迭代牛顿型方法。在每次迭代中，解决信赖域约束线性规划问题来估计活动集。随后，求解置信域等式约束二次规划问题，以获得促进局部超线性收敛的步骤。此类方法对于大规模非线性规划的未来研究具有几个有吸引力的特性。然而，很少发现可用于研究的 SLEQP 方法的实现。为此，我们提出了 pySLEQP，这是 SLEQP 方法在 Python 中的实现。使用非线性规划问题的 CUTEst 和 CUTEr 基准集合来评估该方法和我们的实现的性能和鲁棒性。发现 pySLEQP 显示出鲁棒的行为和合理的性能。
We present a prototype implementation of a Sequential Linear Equality-Constrained Qudratic Programming (SLEQP) method for solving the nonlinear programming problem. Similar to SQP active set methods, SLEQP methods are iterative Newton-type methods. In every iteration, a trust region constrained linear programming problem is solved to estimate the active set. Subsequently, a trust region equality constrained quadratic programming problem is solved to obtain a step that promotes locally superlinear convergence. This class of methods has several appealing properties for future research in large-scale nonlinear programming. Implementations of SLEQP methods accessible for research, however, are scarcely found. To this end, we presentpySLEQP, an implementation of an SLEQP method in Python. The performance and robustness of the method and our implementation are assessed using the CUTEst and CUTEr benchmark collections of nonlinear programming problems.pySLEQPis found to show robust behavior and reasonable performance.
通过 QP-Diving 求解混合整数非线性规划
DOI：
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发表时间：
2012
期刊：
影响因子：
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作者：
Ashutosh Mahajan;S. Leyffer;C. Kirches
通讯作者：
C. Kirches