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1.
This paper deals with exponential neighborhoods for combinatorial optimization problems. Exponential neighborhoods are large sets of feasible solutions whose size grows
exponentially with the input length. We are especially interested in exponential neighborhoods over which the TSP (respectively,
the QAP) can be solved in polynomial time, and we investigate combinatorial and algorithmical questions related to such neighborhoods.?First,
we perform a careful study of exponential neighborhoods for the TSP. We investigate neighborhoods that can be defined in a
simple way via assignments, matchings in bipartite graphs, partial orders, trees and other combinatorial structures. We identify
several properties of these combinatorial structures that lead to polynomial time optimization algorithms, and we also provide
variants that slightly violate these properties and lead to NP-complete optimization problems. Whereas it is relatively easy
to find exponential neighborhoods over which the TSP can be solved in polynomial time, the corresponding situation for the
QAP looks pretty hopeless: Every exponential neighborhood that is considered in this paper provably leads to an NP-complete optimization problem for the QAP.
Received: September 5, 1997 / Accepted: November 15, 1999?Published online February 23, 2000 相似文献
2.
Self-regular functions and new search directions for linear and semidefinite optimization 总被引:11,自引:0,他引:11
In this paper, we introduce the notion of a self-regular function. Such a function is strongly convex and smooth coercive on its domain, the positive real axis. We show that any
such function induces a so-called self-regular proximity function and a corresponding search direction for primal-dual path-following
interior-point methods (IPMs) for solving linear optimization (LO) problems. It is proved that the new large-update IPMs enjoy
a polynomial ?(n
log) iteration bound, where q≥1 is the so-called barrier degree of the kernel function underlying the algorithm. The constant hidden in the ?-symbol depends
on q and the growth degree p≥1 of the kernel function. When choosing the kernel function appropriately the new large-update IPMs have a polynomial ?(lognlog) iteration bound, thus improving the currently best known bound for large-update methods by almost a factor . Our unified analysis provides also the ?(log) best known iteration bound of small-update IPMs. At each iteration, we need to solve only one linear system. An extension
of the above results to semidefinite optimization (SDO) is also presented.
Received: March 2000 / Accepted: December 2001?Published online April 12, 2002 相似文献
3.
H. Hu 《Mathematical Programming》2000,88(2):277-284
This paper studies the existence of a uniform global error bound when a system of linear inequalities is under local arbitrary
perturbations. Specifically, given a possibly infinite system of linear inequalities satisfying the Slater’s condition and
a certain compactness condition, it is shown that for sufficiently small arbitrary perturbations the perturbed system is solvable
and there exists a uniform global error bound if and only if the original system is bounded or its homogeneous system has
a strict solution.
Received: April 12, 1998 / Accepted: February 11, 2000?Published online July 20, 2000 相似文献
4.
Song Xu 《Mathematical Programming》2000,87(3):501-517
We propose an infeasible non-interior path-following method for nonlinear complementarity problems with uniform P-functions. This method is based on the smoothing techniques introduced by Kanzow. A key to our analysis is the introduction
of a new notion of neighborhood for the central path which is suitable for infeasible non-interior path-following methods.
By restricting the iterates in the neighborhood of the central path, we provide a systematic procedure to update the smoothing
parameter and establish the global linear convergence of this method. Some preliminary computational results are reported.
Received: March 13, 1997 / Accepted: December 17, 1999?Published online February 23, 2000 相似文献
5.
Based on the authors’ previous work which established theoretical foundations of two, conceptual, successive convex relaxation
methods, i.e., the SSDP (Successive Semidefinite Programming) Relaxation Method and the SSILP (Successive Semi-Infinite Linear Programming)
Relaxation Method, this paper proposes their implementable variants for general quadratic optimization problems. These problems
have a linear objective function c
T
x to be maximized over a nonconvex compact feasible region F described by a finite number of quadratic inequalities. We introduce two new techniques, “discretization” and “localization,”
into the SSDP and SSILP Relaxation Methods. The discretization technique makes it possible to approximate an infinite number
of semi-infinite SDPs (or semi-infinite LPs) which appeared at each iteration of the original methods by a finite number of
standard SDPs (or standard LPs) with a finite number of linear inequality constraints. We establish:?•Given any open convex set U containing F, there is an implementable discretization of the SSDP (or SSILP) Relaxation Method
which generates a compact convex set C such that F⊆C⊆U in a finite number of iterations.?The localization technique is for the cases where we are only interested in upper bounds on the optimal objective value (for
a fixed objective function vector c) but not in a global approximation of the convex hull of F. This technique allows us to generate a convex relaxation of F that is accurate only in certain directions in a neighborhood of the objective direction c. This cuts off redundant work to make the convex relaxation accurate in unnecessary directions. We establish:?•Given any positive number ε, there is an implementable localization-discretization of the SSDP (or SSILP) Relaxation Method
which generates an upper bound of the objective value within ε of its maximum in a finite number of iterations.
Received: June 30, 1998 / Accepted: May 18, 2000?Published online September 20, 2000 相似文献
6.
Inexact implicit methods for monotone general variational inequalities 总被引:32,自引:0,他引:32
Bingsheng He 《Mathematical Programming》1999,86(1):199-217
Solving a variational inequality problem is equivalent to finding a solution of a system of nonsmooth equations. Recently,
we proposed an implicit method, which solves monotone variational inequality problem via solving a series of systems of nonlinear
smooth (whenever the operator is smooth) equations. It can exploit the facilities of the classical Newton–like methods for
smooth equations. In this paper, we extend the method to solve a class of general variational inequality problems Moreover, we improve the implicit method to allow inexact solutions of the systems of nonlinear equations at each iteration.
The method is shown to preserve the same convergence properties as the original implicit method.
Received July 31, 1995 / Revised version received January 15, 1999? Published online May 28, 1999 相似文献
7.
We propose a class of non-interior point algorithms for solving the complementarity problems(CP): Find a nonnegative pair
(x,y)∈ℝ
2n
satisfying y=f(x) and x
i
y
i
=0 for every i∈{1,2,...,n}, where f is a continuous mapping from ℝ
n
to ℝ
n
. The algorithms are based on the Chen-Harker-Kanzow-Smale smoothing functions for the CP, and have the following features;
(a) it traces a trajectory in ℝ
3n
which consists of solutions of a family of systems of equations with a parameter, (b) it can be started from an arbitrary
(not necessarily positive) point in ℝ
2n
in contrast to most of interior-point methods, and (c) its global convergence is ensured for a class of problems including
(not strongly) monotone complementarity problems having a feasible interior point. To construct the algorithms, we give a
homotopy and show the existence of a trajectory leading to a solution under a relatively mild condition, and propose a class
of algorithms involving suitable neighborhoods of the trajectory. We also give a sufficient condition on the neighborhoods
for global convergence and two examples satisfying it.
Received April 9, 1997 / Revised version received September 2, 1998? Published online May 28, 1999 相似文献
8.
Summary. Solving a variational inequality problem VI(Ω,F) is equivalent to finding a solution of a system of nonsmooth equations (a hard problem). The Peaceman-Rachford and /or Douglas-Rachford operator splitting methods are advantageous when they are applied to solve variational inequality problems, because they solve the original problem via solving a series of systems of nonlinear smooth equations (a series of easy problems). Although the solution of VI(Ω,F) is invariant under multiplying F by some positive scalar β, yet the numerical experiment has shown that the number of iterations depends significantly on the positive parameter β which is a constant in the original operator splitting methods. In general, it is difficult to choose a proper parameter β for individual problems. In this paper, we present a modified operator splitting method which adjusts the scalar parameter automatically per iteration based on the message of the iterates. Exact and inexact forms of the modified method with self-adaptive variable parameter are suggested and proved to be convergent under mild assumptions. Finally, preliminary numerical tests show that the self-adaptive adjustment rule is proper and necessary in practice. 相似文献
9.
1 , the smallest eigenvalue of a symmetric, positive definite matrix, and is solved by Newton iteration with line search. The
paper describes the algorithm and its implementation including estimation of λ1, how to get a good starting point for the iteration, and up- and downdating of Cholesky factorization. Results of extensive
testing and comparison with other methods for constrained QP are given.
Received May 1, 1997 / Revised version received March 17, 1998 Published online November 24, 1998 相似文献
10.
Chun-Hua Guo 《Numerical Functional Analysis & Optimization》2013,34(5):516-529
We start with a discussion of coupled algebraic Riccati equations arising in the study of linear-quadratic optimal control problems for Markov jump linear systems. Under suitable assumptions, this system of equations has a unique positive semidefinite solution, which is the solution of practical interest. The coupled equations can be rewritten as a single linearly perturbed matrix Riccati equation with special structures. We study the linearly perturbed Riccati equation in a more general setting and obtain a class of iterative methods from different splittings of a positive operator involved in the Riccati equation. We prove some special properties of the sequences generated by these methods and determine and compare the convergence rates of these methods. Our results are then applied to the coupled Riccati equations of jump linear systems. We obtain linear convergence of the Lyapunov iteration and the modified Lyapunov iteration, and confirm that the modified Lyapunov iteration indeed has faster convergence than the original Lyapunov iteration. 相似文献
11.
Entropic proximal decomposition methods for convex programs and variational inequalities 总被引:2,自引:0,他引:2
We consider convex optimization and variational inequality problems with a given separable structure. We propose a new decomposition
method for these problems which combines the recent logarithmic-quadratic proximal theory introduced by the authors with a
decomposition method given by Chen-Teboulle for convex problems with particular structure. The resulting method allows to
produce for the first time provably convergent decomposition schemes based on C
∞ Lagrangians for solving convex structured problems. Under the only assumption that the primal-dual problems have nonempty
solution sets, global convergence of the primal-dual sequences produced by the algorithm is established.
Received: October 6, 1999 / Accepted: February 2001?Published online September 17, 2001 相似文献
12.
13.
G. I. Shishkin 《Computational Mathematics and Mathematical Physics》2008,48(6):956-974
The boundary value problem for a singularly perturbed parabolic convection-diffusion equation is considered. A finite difference scheme on a priori (sequentially) adapted grids is constructed and its convergence is examined. The construction of the scheme on a priori adapted grids is based on a majorant of the singular component of the grid solution that makes it possible to a priori find a subdomain in which the grid solution should be further refined given the perturbation parameter ε, the size of the uniform mesh in x, the desired accuracy of the grid solution, and the prescribed number of iterations K used to refine the solution. In the subdomains where the solution is refined, the grid problems are solved on uniform grids. The error of the solution thus constructed weakly depends on ε. The scheme converges almost ε-uniformly; namely, it converges under the condition N ?1 = o(εv), where v = v(K) can be chosen arbitrarily small when K is sufficiently large. If a piecewise uniform grid is used instead of a uniform one at the final Kth iteration, the difference scheme converges ε-uniformly. For this piecewise uniform grid, the ratio of the mesh sizes in x on the parts of the mesh with a constant size (outside the boundary layer and inside it) is considerably less than that for the known ε-uniformly convergent schemes on piecewise uniform grids. 相似文献
14.
On the core of ordered submodular cost games 总被引:5,自引:0,他引:5
A general ordertheoretic linear programming model for the study of matroid-type greedy algorithms is introduced. The primal
restrictions are given by so-called weakly increasing submodular functions on antichains. The LP-dual is solved by a Monge-type
greedy algorithm. The model offers a direct combinatorial explanation for many integrality results in discrete optimization.
In particular, the submodular intersection theorem of Edmonds and Giles is seen to extend to the case with a rooted forest
as underlying structure. The core of associated polyhedra is introduced and applications to the existence of the core in cooperative
game theory are discussed.
Received: November 2, 1995 / Accepted: September 15, 1999?Published online February 23, 2000 相似文献
15.
A new look at smoothing Newton methods for nonlinear complementarity problems and box constrained variational inequalities 总被引:17,自引:0,他引:17
In this paper we take a new look at smoothing Newton methods for solving the nonlinear complementarity problem (NCP) and the
box constrained variational inequalities (BVI). Instead of using an infinite sequence of smoothing approximation functions,
we use a single smoothing approximation function and Robinson’s normal equation to reformulate NCP and BVI as an equivalent
nonsmooth equation H(u,x)=0, where H:ℜ
2n
→ℜ
2n
, u∈ℜ
n
is a parameter variable and x∈ℜ
n
is the original variable. The central idea of our smoothing Newton methods is that we construct a sequence {z
k
=(u
k
,x
k
)} such that the mapping H(·) is continuously differentiable at each z
k
and may be non-differentiable at the limiting point of {z
k
}. We prove that three most often used Gabriel-Moré smoothing functions can generate strongly semismooth functions, which
play a fundamental role in establishing superlinear and quadratic convergence of our new smoothing Newton methods. We do not
require any function value of F or its derivative value outside the feasible region while at each step we only solve a linear system of equations and if
we choose a certain smoothing function only a reduced form needs to be solved. Preliminary numerical results show that the
proposed methods for particularly chosen smoothing functions are very promising.
Received June 23, 1997 / Revised version received July 29, 1999?Published online December 15, 1999 相似文献
16.
For a polytope in the [0,1]
n
cube, Eisenbrand and Schulz showed recently that the maximum Chvátal rank is bounded above by O(n
2logn) and bounded below by (1+ε)n for some ε>0. Chvátal cuts are equivalent to Gomory fractional cuts, which are themselves dominated by Gomory mixed integer
cuts. What do these upper and lower bounds become when the rank is defined relative to Gomory mixed integer cuts? An upper
bound of n follows from existing results in the literature. In this note, we show that the lower bound is also equal to n. This result still holds for mixed 0,1 polyhedra with n binary variables.
Received: March 15, 2001 / Accepted: July 18, 2001?Published online September 17, 2001 相似文献
17.
A system of two coupled singularly perturbed convection–diffusion ordinary differential equations is examined. The diffusion
term in each equation is multiplied by a small parameter, and the equations are coupled through their convective terms. The
problem does not satisfy a conventional maximum principle. Its solution is decomposed into regular and layer components. Bounds
on the derivatives of these components are established that show explicitly their dependence on the small parameter. A numerical
method consisting of simple upwinding and an appropriate piecewise-uniform Shishkin mesh is shown to generate numerical approximations
that are essentially first order convergent, uniformly in the small parameter, to the true solution in the discrete maximum
norm.
相似文献
18.
D. Han 《Journal of Optimization Theory and Applications》2007,132(2):227-243
The Peaceman-Rachford and Douglas-Rachford operator splitting methods are advantageous for solving variational inequality
problems, since they attack the original problems via solving a sequence of systems of smooth equations, which are much easier
to solve than the variational inequalities. However, solving the subproblems exactly may be prohibitively difficult or even
impossible. In this paper, we propose an inexact operator splitting method, where the subproblems are solved approximately
with some relative error tolerance. Another contribution is that we adjust the scalar parameter automatically at each iteration
and the adjustment parameter can be a positive constant, which makes the methods more practical and efficient. We prove the
convergence of the method and present some preliminary computational results, showing that the proposed method is promising.
This work was supported by the NSFC grant 10501024. 相似文献
19.
We consider a class of non-linear mixed integer programs with n integer variables and k continuous variables. Solving instances from this class to optimality is an NP-hard problem. We show that for the cases with
k=1 and k=2, every optimal solution is integral. In contrast to this, for every k≥3 there exist instances where every optimal solution takes non-integral values.
Received: August 2001 / Accepted: January 2002?Published online March 27, 2002 相似文献
20.
The dynamical characteristics of scalar difference equations of the form?x
n
+1=f
1(x
n
−τ1)+f
2(x
n
−τ2), n=0,1,2,...,?are investigated. A necessary and sufficient condition is obtained for all positive solutions to be oscillatory
about a unique positive equilibrium point and sufficient criteria for the global attractivity of the equilibrium are established.
Also, the stability and periodicity of more general equations are studied via comparison with the corresponding properties
of an associated first-order non-linear equation.
Received: September 5, 2001; in final form: March 7, 2002?Published online: April 14, 2003 相似文献