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1.
Stephen J. Wright 《Mathematical Programming》2003,95(1):137-160
In the vicinity of a solution of a nonlinear programming problem at which both strict complementarity and linear independence
of the active constraints may fail to hold, we describe a technique for distinguishing weakly active from strongly active
constraints. We show that this information can be used to modify the sequential quadratic programming algorithm so that it
exhibits superlinear convergence to the solution under assumptions weaker than those made in previous analyses.
Received: December 18, 2000 / Accepted: January 14, 2002 Published online: September 27, 2002
RID="★"
ID="★" Research supported by the Mathematical, Information, and Computational Sciences Division subprogram of the Office of
Advanced Scientific Computing Research, U.S. Department of Energy, under Contract W-31-109-Eng-38.
Key words. nonlinear programming problems – degeneracy – active constraint identification – sequential quadratic programming 相似文献
2.
Non-Interior continuation methods for solving semidefinite complementarity problems 总被引:13,自引:0,他引:13
There recently has been much interest in non-interior continuation/smoothing methods for solving linear/nonlinear complementarity
problems. We describe extensions of such methods to complementarity problems defined over the cone of block-diagonal symmetric
positive semidefinite real matrices. These extensions involve the Chen-Mangasarian class of smoothing functions and the smoothed
Fischer-Burmeister function. Issues such as existence of Newton directions, boundedness of iterates, global convergence, and
local superlinear convergence will be studied. Preliminary numerical experience on semidefinite linear programs is also reported.
Received: October 1999 / Accepted: April 2002 Published online: December 19, 2002
RID="⋆"
ID="⋆" This research is supported by National Science Foundation Grant CCR-9731273.
Key words. semidefinite complementarity problem – smoothing function – non-interior continuation – global convergence – local superlinear
convergence 相似文献
3.
We consider optimality systems of Karush-Kuhn-Tucker (KKT) type, which arise, for example, as primal-dual conditions characterizing
solutions of optimization problems or variational inequalities. In particular, we discuss error bounds and Newton-type methods
for such systems. An exhaustive comparison of various regularity conditions which arise in this context is given. We obtain
a new error bound under an assumption which we show to be strictly weaker than assumptions previously used for KKT systems,
such as quasi-regularity or semistability (equivalently, the R
0-property). Error bounds are useful, among other things, for identifying active constraints and developing efficient local
algorithms. We propose a family of local Newton-type algorithms. This family contains some known active-set Newton methods,
as well as some new methods. Regularity conditions required for local superlinear convergence compare favorably with convergence
conditions of nonsmooth Newton methods and sequential quadratic programming methods.
Received: December 10, 2001 / Accepted: July 28, 2002 Published online: February 14, 2003
Key words. KKT system – regularity – error bound – active constraints – Newton method
Mathematics Subject Classification (1991): 90C30, 65K05 相似文献
4.
We consider a quadratic cut method based on analytic centers for two cases of convex quadratic feasibility problems. In the
first case, the convex set is defined by a finite yet large number, N, of convex quadratic inequalities. We extend quadratic cut algorithm of Luo and Sun [3] for solving such problems by placing
or translating the quadratic cuts directly through the current approximate center. We show that, in terms of total number
of addition and translation of cuts, our algorithm has the same polynomial worst case complexity as theirs [3]. However, the
total number of steps, where steps consist of (damped) Newton steps, function evaluations and arithmetic operations, required
to update from one approximate center to another is , where ε is the radius of the largest ball contained in the feasible set. In the second case, the convex set is defined by
an infinite number of certain strongly convex quadratic inequalities. We adapt the same quadratic cut method for the first
case to the second one. We show that in this case the quadratic cut algorithm is a fully polynomial approximation scheme.
Furthermore, we show that, at each iteration, k, the total number steps (as described above) required to update from one approximate center to another is at most , with ε as defined above.
Received: April 2000 / Accepted: June 2002 Published online: September 5, 2002
Key words. convex quadratic feasibility problem – interior-point methods – analytic center – quadratic cuts – potential function 相似文献
5.
Non-monotone trust region methods for nonlinear equality constrained optimization without a penalty function 总被引:3,自引:0,他引:3
We propose and analyze a class of penalty-function-free nonmonotone trust-region methods for nonlinear equality constrained
optimization problems. The algorithmic framework yields global convergence without using a merit function and allows nonmonotonicity
independently for both, the constraint violation and the value of the Lagrangian function. Similar to the Byrd–Omojokun class
of algorithms, each step is composed of a quasi-normal and a tangential step. Both steps are required to satisfy a decrease
condition for their respective trust-region subproblems. The proposed mechanism for accepting steps combines nonmonotone decrease
conditions on the constraint violation and/or the Lagrangian function, which leads to a flexibility and acceptance behavior
comparable to filter-based methods. We establish the global convergence of the method. Furthermore, transition to quadratic
local convergence is proved. Numerical tests are presented that confirm the robustness and efficiency of the approach.
Received: December 14, 2000 / Accepted: August 30, 2001 Published online: September 27, 2002
Key words. nonmonotone trust-region methods – sequential quadratic programming – penalty function – global convergence – equality constraints
– local convergence – large-scale optimization
Mathematics Subject Classification (2000): 65K05, 90C30 相似文献
6.
Combining search directions using gradient flows 总被引:2,自引:0,他引:2
The efficient combination of directions is a significant problem in line search methods that either use negative curvature,
or wish to include additional information such as the gradient or different approximations to the Newton direction.
In this paper we describe a new procedure to combine several of these directions within an interior-point primal-dual algorithm.
Basically, we combine in an efficient manner a modified Newton direction with the gradient of a merit function and a direction
of negative curvature, if it exists. We also show that the procedure is well-defined, and it has reasonable theoretical properties
regarding the rate of convergence of the method.
We also present numerical results from an implementation of the proposed algorithm on a set of small test problems from the
CUTE collection.
Received: November 2000 / Accepted: October 2002 Published online: February 14, 2003
Key Words. negative curvature – primal-dual methods – interior-point methods – nonconvex optimization – line searches
Mathematics Subject Classification (1991): 49M37, 65K05, 90C30 相似文献
7.
Andreas Fischer 《Mathematical Programming》2002,94(1):91-124
An iterative framework for solving generalized equations with nonisolated solutions is presented. For generalized equations
with the structure , where is a multifunction and F is single-valued, the framework covers methods that, at each step, solve subproblems of the type . The multifunction approximates F around s. Besides a condition on the quality of this approximation, two other basic assumptions are employed to show Q-superlinear
or Q-quadratic convergence of the iterates to a solution. A key assumption is the upper Lipschitz-continuity of the solution
set map of the perturbed generalized equation . Moreover, the solvability of the subproblems is required. Conditions that ensure these assumptions are discussed in general
and by means of several applications. They include monotone mixed complementarity problems, Karush-Kuhn-Tucker systems arising
from nonlinear programs, and nonlinear equations. Particular results deal with error bounds and upper Lipschitz-continuity
properties for these problems.
Received: November 2001 / Accepted: November 2002 Published online: December 9, 2002
Key Words. generalized equation – nonisolated solutions – Newton's method – superlinear convergence – upper Lipschitz-continuity – mixed
complementarity problem – error bounds
Mathematics Subject Classification (1991): 90C30, 65K05, 90C31, 90C33 相似文献
8.
Graph partition is used in the telecommunication industry to subdivide a transmission network into small clusters. We consider
both linear and semidefinite relaxations for the equipartition problem and present numerical results on real data from France
Telecom networks with up 900 nodes, and also on randomly generated problems.
Received: August 8, 2001 / Accepted: November 9, 2001 Published online: December 9, 2002
RID="★★"
ID="★★" This research was carried out while this author was working at France Telecom R & D, 38–40 rue du Général Leclerc,
F-92794 Issy-Les-Moulineaux Cedex 9, France.
RID="★"
ID="★" This author greatfully acknowledges financial support from the Austrian Science Foundation FWF Project P12660-MAT.
Key words. graph partitioning – semidefinite programming 相似文献
9.
10.
In this paper we consider stochastic programming problems where the objective function is given as an expected value of a
convex piecewise linear random function. With an optimal solution of such a problem we associate a condition number which
characterizes well or ill conditioning of the problem. Using theory of Large Deviations we show that the sample size needed
to calculate the optimal solution of such problem with a given probability is approximately proportional to the condition
number.
Received: May 2000 / Accepted: May 2002-07-16 Published online: September 5, 2002
RID="★"
The research of this author was supported, in part, by grant DMS-0073770 from the National Science Foundation
Key Words. stochastic programming – Monte Carlo simulation – large deviations theory – ill-conditioned problems 相似文献
11.
Wang Qinggang Zhao Jinling Yang Qingzhi 《Computational Optimization and Applications》2010,46(1):31-49
In this paper we present some non-interior path-following methods for linear complementarity problems. Instead of using the
standard central path we use a scaled central path. Based on this new central path, we first give a feasible non-interior
path-following method for linear complementarity problems. And then we extend it to an infeasible method. After proving the
boundedness of the neighborhood, we prove the convergence of our method. Another point we should present is that we prove
the local quadratic convergence of feasible method without the assumption of strict complementarity at the solution. 相似文献
12.
Convergence rate analysis of iteractive algorithms for solving variational inequality problems 总被引:3,自引:0,他引:3
M.V. Solodov 《Mathematical Programming》2003,96(3):513-528
We present a unified convergence rate analysis of iterative methods for solving the variational inequality problem. Our results
are based on certain error bounds; they subsume and extend the linear and sublinear rates of convergence established in several
previous studies. We also derive a new error bound for $\gamma$-strictly monotone variational inequalities. The class of algorithms
covered by our analysis in fairly broad. It includes some classical methods for variational inequalities, e.g., the extragradient,
matrix splitting, and proximal point methods. For these methods, our analysis gives estimates not only for linear convergence
(which had been studied extensively), but also sublinear, depending on the properties of the solution. In addition, our framework
includes a number of algorithms to which previous studies are not applicable, such as the infeasible projection methods, a
separation-projection method, (inexact) hybrid proximal point methods, and some splitting techniques. Finally, our analysis
covers certain feasible descent methods of optimization, for which similar convergence rate estimates have been recently obtained
by Luo [14].
Received: April 17, 2001 / Accepted: December 10, 2002
Published online: April 10, 2003
RID="⋆"
ID="⋆" Research of the author is partially supported by CNPq Grant 200734/95–6, by PRONEX-Optimization, and by FAPERJ.
Key Words. Variational inequality – error bound – rate of convergence
Mathematics Subject Classification (2000): 90C30, 90C33, 65K05 相似文献
13.
On implementing a primal-dual interior-point method for conic quadratic optimization 总被引:8,自引:0,他引:8
Based on the work of the Nesterov and Todd on self-scaled cones an implementation of a primal-dual interior-point method
for solving large-scale sparse conic quadratic optimization problems is presented. The main features of the implementation
are it is based on a homogeneous and self-dual model, it handles rotated quadratic cones directly, it employs a Mehrotra type
predictor-corrector extension and sparse linear algebra to improve the computational efficiency. Finally, the implementation
exploits fixed variables which naturally occurs in many conic quadratic optimization problems. This is a novel feature for
our implementation. Computational results are also presented to document that the implementation can solve very large problems
robustly and efficiently.
Received: November 18, 2000 / Accepted: January 18, 2001 Published online: September 27, 2002
Key Words. conic optimization – interior-point methods – large-scale implementation 相似文献
14.
In this paper, we propose a modified semismooth Newton method for a class of complementarity problems arising from the discretization of free boundary problems and establish its monotone convergence. We show that under appropriate conditions, the method reduces to semismooth Newton method. We also do some preliminary numerical experiments to show the efficiency of the proposed method. 相似文献
15.
Renato. D. C. Monteiro 《Mathematical Programming》2003,97(1-2):209-244
In this paper, we survey the most recent methods that have been developed for the solution of semidefinite programs. We first
concentrate on the methods that have been primarily motivated by the interior point (IP) algorithms for linear programming,
putting special emphasis in the class of primal-dual path-following algorithms. We also survey methods that have been developed
for solving large-scale SDP problems. These include first-order nonlinear programming (NLP) methods and more specialized path-following
IP methods which use the (preconditioned) conjugate gradient or residual scheme to compute the Newton direction and the notion
of matrix completion to exploit data sparsity.
Received: December 16, 2002 / Accepted: May 5, 2003
Published online: May 28, 2003
Key words. semidefinite programming – interior-point methods – polynomial complexity – path-following methods – primal-dual methods
– nonlinear programming – Newton method – first-order methods – bundle method – matrix completion
The author's research presented in this survey article has been supported in part by NSF through grants INT-9600343, INT-9910084,
CCR-9700448, CCR-9902010, CCR-0203113 and ONR through grants N00014-93-1-0234, N00014-94-1-0340 and N00014-03-1-0401.
Mathematics Subject Classification (2000): 65K05, 90C06, 90C22, 90C25, 90C30, 90C51 相似文献
16.
Hannes Buchholzer Christian Kanzow Peter Knabner Serge Kräutle 《Computational Optimization and Applications》2011,50(2):193-221
The semismooth Newton method was introduced in a paper by Qi and Sun (Math. Program. 58:353–367, 1993) and the subsequent work by Qi (Math. Oper. Res. 18:227–244, 1993). This method became the basis of many solvers for certain classes of nonlinear systems of equations defined by a nonsmooth
mapping. Here we consider a particular system of equations that arises from the discretization of a reactive transport model
in the subsurface including mineral precipitation-dissolution reactions. The model is highly complicated and uses a coupling
of PDEs, ODEs, and algebraic equations, together with some complementarity conditions arising from the equilibrium conditions
of the minerals. The aim is to show that this system, though quite complicated, usually satisfies the convergence criteria
for the semismooth Newton method, and can therefore be solved by a locally quadratically convergent method. This gives a theoretical
sound approach for the solution of this kind of applications, whereas the geoscientist’s community most frequently applies
algorithms involving some kind of trial-and-error strategies. 相似文献
17.
Shaohua Pan Jein-Shan Chen Sangho Kum Yongdo Lim 《Computational Optimization and Applications》2011,49(3):457-491
In this paper, we study the properties of the penalized Fischer-Burmeister (FB) second-order cone (SOC) complementarity function.
We show that the function possesses similar desirable properties of the FB SOC complementarity function for local convergence;
for example, with the function the second-order cone complementarity problem (SOCCP) can be reformulated as a (strongly) semismooth
system of equations, and the corresponding nonsmooth Newton method has local quadratic convergence without strict complementarity
of solutions. In addition, the penalized FB merit function has bounded level sets under a rather weak condition which can
be satisfied by strictly feasible monotone SOCCPs or SOCCPs with the Cartesian R
01-property, although it is not continuously differentiable. Numerical results are included to illustrate the theoretical considerations. 相似文献
18.
We revise the Volume Algorithm (VA) for linear programming and relate it to bundle methods. When first introduced, VA was
presented as a subgradient-like method for solving the original problem in its dual form. In a way similar to the serious/null
steps philosophy of bundle methods, VA produces green, yellow or red steps. In order to give convergence results, we introduce
in VA a precise measure for the improvement needed to declare a green or serious step. This addition yields a revised formulation
(RVA) that is halfway between VA and a specific bundle method, that we call BVA. We analyze the convergence properties of
both RVA and BVA. Finally, we compare the performance of the modified algorithms versus VA on a set of Rectilinear Steiner
problems of various sizes and increasing complexity, derived from real world VLSI design instances.
Received: December 1999 / Accepted: September 2002 Published online: December 19, 2002
Key Words. volume algorithm – bundle methods – Steiner problems
Correspondence to: Claudia A. Sagastizábal, e-mail: sagastiz@impa.br 相似文献
19.
In this paper, we present a hybrid method for the solution of a class of composite semismooth equations encountered frequently in applications. The method is obtained by combining a generalized finite-difference Newton method to an inexpensive direct search method. We prove that, under standard assumptions, the method is globally convergent with a local rate of convergence which is superlinear or quadratic. We report also several numerical results obtained applying the method to suitable reformulations of well-known nonlinear complementarity problems. 相似文献
20.
The stability number α(G) for a given graph G is the size of a maximum stable set in G. The Lovász theta number provides an upper bound on α(G) and can be computed in polynomial time as the optimal value of the Lovász semidefinite program. In this paper, we show that
restricting the matrix variable in the Lovász semidefinite program to be rank-one and rank-two, respectively, yields a pair
of continuous, nonlinear optimization problems each having the global optimal value α(G). We propose heuristics for obtaining large stable sets in G based on these new formulations and present computational results indicating the effectiveness of the heuristics.
Received: December 13, 2000 / Accepted: September 3, 2002 Published online: December 19, 2002
RID="★"
ID="★" Computational results reported in this paper were obtained on an SGI Origin2000 computer at Rice University acquired
in part with support from NSF Grant DMS-9872009.
Key Words. maximum stable set – maximum clique – minimum vertex cover – semidefinite program – semidefinite relaxation – continuous
optimization heuristics – nonlinear programming
Mathematics Subject Classification (2000): 90C06, 90C27, 90C30 相似文献