R. Fletcher. The sequential quadratic programming method. Number pp. 165-214 in Nonlinear Optimization, Lecture Notes in Mathematics,. Springer Berlin Heidelberg, 2010.Fletcher, R. (2010). The sequential quadratic programming method, in G. Di Pillo and F. Schoen (Eds.), Nonlinear Optimization,...
The Sequential Quadratic Programming Method Roger Fletcher 1 Introduction Sequential (or Successive) Quadratic Programming (SQP) is a technique for the solution of Nonlinear Programming (NLP) problems. It is, as we shall see, an idealized concept, permitting and indeed necessitating many variations ...
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to solve this problem, among them: the bee colony optimization and the sequential quadratic programming (BCO-SQP) in [11], the EP-SQP in [2], the PSO-SQP in [7], the modified hybrid EP-SQP (MHEP-SQP) in [12], and the genetic algorithm method with differential evolution in [9]....
Lagrange-Newton methodsequential quadratic programminginfinite-dimensional optimizationThis paper investigates local convergence properties of the Lagrange-Newton method for optimization problems in reflexive Banach spaces. Sufficient conditions for quadratic convergence of optimal solutions and Lagrange multipliers ...
The optimization was performed using thefminconfunction in Matlab to minimize the cost function shown above using the sequential quadratic programming (sqp) algorithm. There were no additional constraints imposed on this optimization. The results shown in Figs.2–4contain results from an optimized eight...
最有这个问题被构建成了非线性优化问题,通过SQP求解,说实话在论文推土机:Robust Online Path Planning for Autonomous Vehicle Using Sequential Quadratic Programming我们就尝试了SQP的方案,他的最大的问题在于耗时比QP显著增加,需要通过很多工程化方法进行加速,经验告诉我们能用QP别用SQP。所以这部分的非线性优化问题我认...
This approach would be tantamount to having converted the MIP problem into a nonlinear programming (NLP) problem. This paper describes the application of a sequential quadratic programming (SQP) technique in design optimization for an induction motor. It demonstrates how a MIP-problem can be ...
12. The non-linear optimization problem is solved by sequential quadratic programming with the use of finite difference gradients. The results of the parametric optimization are summarized in Supplementary Table 1 and Supplementary Table 4. From the first of the two tables, it is seen that the ...
It was then solved with a common gradient-decent optimization algorithm based on the sequential quadratic programming (SQP) method. Implementing Gxx and Rxx into the optimization criterion can be understood as weighting each of Eq. (29a), (29b), (29c) with its corresponding contributing moment ...