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1.
An algorithm of sequential systems of linear equations for nonlinear optimization problems with arbitrary initial point 总被引:6,自引:0,他引:6
For current sequential quadratic programming (SQP) type algorithms, there exist two problems: (i) in order to obtain a search
direction, one must solve one or more quadratic programming subproblems per iteration, and the computation amount of this
algorithm is very large. So they are not suitable for the large-scale problems; (ii) the SQP algorithms require that the related
quadratic programming subproblems be solvable per iteration, but it is difficult to be satisfied. By using ε-active set procedure
with a special penalty function as the merit function, a new algorithm of sequential systems of linear equations for general
nonlinear optimization problems with arbitrary initial point is presented. This new algorithm only needs to solve three systems
of linear equations having the same coefficient matrix per iteration, and has global convergence and local superlinear convergence.
To some extent, the new algorithm can overcome the shortcomings of the SQP algorithms mentioned above.
Project partly supported by the National Natural Science Foundation of China and Tianyuan Foundation of China. 相似文献
2.
In this paper, a new SQP algorithm is presented to solve the general nonlinear programs with mixed equality and inequality constraints. Quoted from P. Spellucci (see [9]), this method maybe be named sequential equality constrained quadratic programming (SECQP) algorithm. Per single iteration, based on an active set strategy ( see [9]), this SECQP algorithm requires only to solve equality constrained quadratic programming subproblems or system of linear equations. The theoretical analysis shows that global and superlinear convergence can be induced under some suitable conditions. 相似文献
3.
A Robust SQP Method for Mathematical Programs with Linear Complementarity Constraints 总被引:1,自引:0,他引:1
The relationship between the mathematical program with linear complementarity constraints (MPLCC) and its inequality relaxation
is studied. Based on this relationship, a new sequential quadratic programming (SQP) method is presented for solving the MPLCC.
A certain SQP technique is introduced to deal with the possible infeasibility of quadratic programming subproblems. Global
convergence results are derived without assuming the linear independence constraint qualification for MPEC, the nondegeneracy
condition, and any feasibility condition of the quadratic programming subproblems. Preliminary numerical results are reported.
Research is partially supported by Singapore-MIT Alliance and School of Business, National University of Singapore. 相似文献
4.
In this paper,a new globally convergent algorithm for nonlinear optimization prablems with equality and inequality constraints is presented. The new algorithm is of SQP type which determines a search direction by solving a quadratic programming subproblem per itera-tion. Some revisions on the quadratic programming subproblem have been made in such a way that the associated constraint region is nonempty for each point x generated by the algorithm, i. e. , the subproblems always have optimal solutions. The new algorithm has two important properties. The computation of revision parameter for guaranteeing the consistency of quadratic sub-problem and the computation of the second order correction step for superlinear convergence use the same inverse of a matrix per iteration, so the computation amount of the new algorithm will not be increased much more than other SQP type algorithms; Another is that the new algorithm can give automatically a feasible point as a starting point for the quadratic subproblems pe 相似文献
5.
Sequential Systems of Linear Equations Algorithm for Nonlinear Optimization Problems with General Constraints 总被引:6,自引:0,他引:6
In Ref. 1, a new superlinearly convergent algorithm of sequential systems of linear equations (SSLE) for nonlinear optimization problems with inequality constraints was proposed. At each iteration, this new algorithm only needs to solve four systems of linear equations having the same coefficient matrix, which is much less than the amount of computation required for existing SQP algorithms. Moreover, unlike the quadratic programming subproblems of the SQP algorithms (which may not have a solution), the subproblems of the SSLE algorithm are always solvable. In Ref. 2, it is shown that the new algorithm can also be used to deal with nonlinear optimization problems having both equality and inequality constraints, by solving an auxiliary problem. But the algorithm of Ref. 2 has to perform a pivoting operation to adjust the penalty parameter per iteration. In this paper, we improve the work of Ref. 2 and present a new algorithm of sequential systems of linear equations for general nonlinear optimization problems. This new algorithm preserves the advantages of the SSLE algorithms, while at the same time overcoming the aforementioned shortcomings. Some numerical results are also reported. 相似文献
6.
SEQUENTIAL SYSTEMS OF LINEAR EQUATIONS ALGORITHM FOR NONLINEAR OPTIMIZATION PROBLEMS-INEQUALITY CONSTRAINED PROBLEMS 总被引:3,自引:0,他引:3
Zi-you Gao 《计算数学(英文版)》2002,(3)
AbstractIn this paper, a new superlinearly convergent algorithm of sequential systems of linear equations (SSLE) for nonlinear optimization problems with inequality constraints is proposed. Since the new algorithm only needs to solve several systems of linear equations having a same coefficient matrix per iteration, the computation amount of the algorithm is much less than that of the existing SQP algorithms per iteration. Moreover, for the SQP type algorithms, there exist so-called inconsistent problems, i.e., quadratic programming subproblems of the SQP algorithms may not have a solution at some iterations, but this phenomenon will not occur with the SSLE algorithms because the related systems of linear equations always have solutions. Some numerical results are reported. 相似文献
7.
On the Global Convergence of a Projective Trust Region Algorithm for Nonlinear Equality Constrained Optimization 下载免费PDF全文
A trust-region sequential quadratic programming (SQP) method is developed and analyzed for the solution of smooth equality constrained optimization problems. The trust-region SQP algorithm is based on filter line search technique and a composite-step approach, which decomposes the overall step as sum of a vertical step and a horizontal step. The algorithm includes critical modifications of horizontal step computation. One orthogonal projective matrix of the Jacobian of constraint functions is employed in trust-region subproblems. The orthogonal projection gives the null space of the transposition of the Jacobian of the constraint function. Theoretical analysis shows that the new algorithm retains the global convergence to the first-order critical points under rather general conditions. The preliminary numerical results are reported. 相似文献
8.
C. Ling L. Q. Qi G. L. Zhou S. Y. Wu 《Journal of Optimization Theory and Applications》2006,129(1):147-164
The semi-infinite programming (SIP) problem is a program with infinitely many constraints. It can be reformulated as a nonsmooth
nonlinear programming problem with finite constraints by using an integral function. Due to the nondifferentiability of the
integral function, gradient-based algorithms cannot be used to solve this nonsmooth nonlinear programming problem. To overcome
this difficulty, we present a robust smoothing sequential quadratic programming (SQP) algorithm for solving the nonsmooth
nonlinear programming problem. At each iteration of the algorthm, we need to solve only a quadratic program that is always
feasible and solvable. The global convergence of the algorithm is established under mild conditions. Numerical results are
given.
Communicated by F. Giannessi
His work was supported by the Hong Kong Research Grant Council
His work was supported by the Australian Research Council. 相似文献
9.
In this paper, a class of general nonlinear programming problems with inequality and equality constraints is discussed. Firstly, the original problem is transformed into an associated simpler equivalent problem with only inequality constraints. Then, inspired by the ideals of the sequential quadratic programming (SQP) method and the method of system of linear equations (SLE), a new type of SQP algorithm for solving the original problem is proposed. At each iteration, the search direction is generated by the combination of two directions, which are obtained by solving an always feasible quadratic programming (QP) subproblem and a SLE, respectively. Moreover, in order to overcome the Maratos effect, the higher-order correction direction is obtained by solving another SLE. The two SLEs have the same coefficient matrices, and we only need to solve the one of them after a finite number of iterations. By a new line search technique, the proposed algorithm possesses global and superlinear convergence under some suitable assumptions without the strict complementarity. Finally, some comparative numerical results are reported to show that the proposed algorithm is effective and promising. 相似文献
10.
11.
《Journal of Computational and Applied Mathematics》2005,180(1):201-211
Sequential quadratic programming (SQP) has been one of the most important methods for solving nonlinearly constrained optimization problems. In this paper, we present and study an active set SQP algorithm for inequality constrained optimization. The active set technique is introduced which results in the size reduction of quadratic programming (QP) subproblems. The algorithm is proved to be globally convergent. Thus, the results show that the global convergence of SQP is still guaranteed by deleting some “redundant” constraints. 相似文献
12.
Chungen Shen Sven Leyffer Roger Fletcher 《Computational Optimization and Applications》2012,52(3):583-607
We propose a new nonmonotone filter method to promote global and fast local convergence for sequential quadratic programming algorithms. Our method uses two filters: a standard, global g-filter for global convergence, and a local nonmonotone l-filter that allows us to establish fast local convergence. We show how to switch between the two filters efficiently, and we prove global and superlinear local convergence. A special feature of the proposed method is that it does not require second-order correction steps. We present preliminary numerical results comparing our implementation with a classical filter SQP method. 相似文献
13.
14.
Mihai Anitescu 《Mathematical Programming》2002,92(2):359-386
We analyze the convergence of a sequential quadratic programming (SQP) method for nonlinear programming for the case in which
the Jacobian of the active constraints is rank deficient at the solution and/or strict complementarity does not hold for some
or any feasible Lagrange multipliers. We use a nondifferentiable exact penalty function, and we prove that the sequence generated
by an SQP using a line search is locally R-linearly convergent if the matrix of the quadratic program is positive definite
and constant over iterations, provided that the Mangasarian-Fromovitz constraint qualification and some second-order sufficiency
conditions hold.
Received: April 28, 1998 / Accepted: June 28, 2001?Published online April 12, 2002 相似文献
15.
16.
非线性约束条件下一个超线性收敛的可行方法 总被引:3,自引:0,他引:3
在本文中,我们对非线性不等式约束条件下的非线性优化问题给出了一个新的SQP类可行方法.此算法不但结构简单、易于计算,并且在适当的假设条件下,我们证明了算法具有全局收敛性及超线性收敛性 相似文献
17.
In this paper, we present a new sequential quadratically constrained quadratic programming (SQCQP) algorithm, in which a simple updating strategy of the penalty parameter is adopted. This strategy generates nonmonotone penalty parameters at early iterations and only uses the multiplier corresponding to the bound constraint of the quadratically constrained quadratic programming (QCQP) subproblem instead of the multipliers of the quadratic constraints, which will bring some numerical advantages. Furthermore, by using the working set technique, we remove the constraints of the QCQP subproblem that are locally irrelevant, and thus the computational cost could be reduced. Without assuming the convexity of the objective function or the constraints, the algorithm is proved to be globally, superlinearly and quadratically convergent. Preliminary numerical results show that the proposed algorithm is very promising when compared with the tested SQP algorithms. 相似文献
18.
Ke Su 《Journal of Global Optimization》2008,41(2):203-217
In this paper, we presented a modified SQP-filter method based on the modified quadratic subproblem proposed by Zhou (J. Global
Optim. 11, 193–2005, 1997). In contrast with the SQP methods, each iteration this algorithm only needs to solve one quadratic
programming subproblems and it is always feasible. Moreover, it has no demand on the initial point. With the filter technique,
the algorithm shows good numerical results. Under some conditions, the globally and superlinearly convergent properties are
given. 相似文献
19.
In this paper, the feasible type SQP method is improved. A new SQP algorithm is presented to solve the nonlinear inequality constrained optimization. As compared with the existing SQP methods, per single iteration, in order to obtain the search direction, it is only necessary to solve equality constrained quadratic programming subproblems and systems of linear equations. Under some suitable conditions, the global and superlinear convergence can be induced. 相似文献
20.
通过将互补问题转化为一种带非负约束的极小化问题 ,给出了求解互补问题的一种序列二次规划方法 .该方法中每一个子问题都是可解的 ,迭代产生的序列是非负的 ,在适当的条件下 ,分别证明了算法的全局收敛性、局部超线收敛性以及局部二次收敛性 . 相似文献