共查询到10条相似文献,搜索用时 109 毫秒
1.
The bin packing problem is one of the classical NP-hard optimization problems. In this paper, we present a simple generic
approach for obtaining new fast lower bounds, based on dual feasible functions. Worst-case analysis as well as computational
results show that one of our classes clearly outperforms the previous best “economical” lower bound for the bin packing problem
by Martello and Toth, which can be understood as a special case. In particular, we prove an asymptotic worst-case performance
of 3/4 for a bound that can be computed in linear time for items sorted by size. In addition, our approach provides a general
framework for establishing new bounds.
Received: August 11, 1998 / Accepted: February 1, 2001?Published online September 17, 2001 相似文献
2.
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
3.
G. Still 《Mathematical Programming》2001,91(1):53-69
The discretization approach for solving semi-infinite optimization problems is considered. We are interested in the convergence
rate of the error between the solution of the semi-infinite problem and the solution of the discretized program depending
on the discretization mesh-size. It will be shown how this rate depends on whether the minimizer is strict of order one or
two and on whether the discretization includes boundary points of the index set in a specific way. This is done for ordinary
and for generalized semi-infinite problems.
Received: November 21, 2000 / Accepted: May 2001?Published online September 17, 2001 相似文献
4.
Martin Skutella 《Mathematical Programming》2002,91(3):493-514
In the single source unsplittable min-cost flow problem, commodities must be routed simultaneously from a common source vertex
to certain destination vertices in a given graph with edge capacities and costs; the demand of each commodity must be routed
along a single path so that the total flow through any edge is at most its capacity. Moreover, the total cost must not exceed
a given budget. This problem has been introduced by Kleinberg [7] and generalizes several NP-complete problems from various
areas in combinatorial optimization such as packing, partitioning, scheduling, load balancing, and virtual-circuit routing.
Kolliopoulos and Stein [9] and Dinitz, Garg, and Goemans [4] developed algorithms improving the first approximation results
of Kleinberg for the problem of minimizing the violation of edge capacities and for other variants. However, known techniques
do not seem to be capable of providing solutions without also violating the cost constraint. We give the first approximation
results with hard cost constraints. Moreover, all our results dominate the best known bicriteria approximations. Finally,
we provide results on the hardness of approximation for several variants of the problem.
Received: August 23, 2000 / Accepted: April 20, 2001?Published online October 2, 2001 相似文献
5.
We consider stochastic programming problems with probabilistic constraints involving integer-valued random variables. The
concept of a p-efficient point of a probability distribution is used to derive various equivalent problem formulations. Next we introduce
the concept of r-concave discrete probability distributions and analyse its relevance for problems under consideration. These notions are
used to derive lower and upper bounds for the optimal value of probabilistically constrained stochastic programming problems
with discrete random variables. The results are illustrated with numerical examples.
Received: October 1998 / Accepted: June 2000?Published online October 18, 2000 相似文献
6.
Trade-off information related to Pareto optimal solutions is important in multiobjective optimization problems with conflicting
objectives. Recently, the concept of trade-off directions has been introduced for convex problems. These trade-offs are characterized
with the help of tangent cones. Generalized trade-off directions for nonconvex problems can be defined by replacing convex
tangent cones with nonconvex contingent cones. Here we study how the convex concepts and results can be generalized into a
nonconvex case. Giving up convexity naturally means that we need local instead of global analysis.
Received: December 2000 / Accepted: October 2001?Published online February 14, 2002 相似文献
7.
The alternating directions method (ADM) is an effective method for solving a class of variational inequalities (VI) when the
proximal and penalty parameters in sub-VI problems are properly selected. In this paper, we propose a new ADM method which
needs to solve two strongly monotone sub-VI problems in each iteration approximately and allows the parameters to vary from
iteration to iteration. The convergence of the proposed ADM method is proved under quite mild assumptions and flexible parameter
conditions.
Received: January 4, 2000 / Accepted: October 2001?Published online February 14, 2002 相似文献
8.
A conic linear system is a system of the form?P(d): find x that solves b - Ax∈C
Y
, x∈C
X
,? where C
X
and C
Y
are closed convex cones, and the data for the system is d=(A,b). This system is“well-posed” to the extent that (small) changes in the data (A,b) do not alter the status of the system (the system remains solvable or not). Renegar defined the “distance to ill-posedness”,
ρ(d), to be the smallest change in the data Δd=(ΔA,Δb) for which the system P(d+Δd) is “ill-posed”, i.e., d+Δd is in the intersection of the closure of feasible and infeasible instances d’=(A’,b’) of P(·). Renegar also defined the “condition measure” of the data instance d as C(d):=∥d∥/ρ(d), and showed that this measure is a natural extension of the familiar condition measure associated with systems of linear
equations. This study presents two categories of results related to ρ(d), the distance to ill-posedness, and C(d), the condition measure of d. The first category of results involves the approximation of ρ(d) as the optimal value of certain mathematical programs. We present ten different mathematical programs each of whose optimal
values provides an approximation of ρ(d) to within certain constants, depending on whether P(d) is feasible or not, and where the constants depend on properties of the cones and the norms used. The second category of
results involves the existence of certain inscribed and intersecting balls involving the feasible region of P(d) or the feasible region of its alternative system, in the spirit of the ellipsoid algorithm. These results roughly state that
the feasible region of P(d) (or its alternative system when P(d) is not feasible) will contain a ball of radius r that is itself no more than a distance R from the origin, where the ratio R/r satisfies R/r≤c
1
C(d), and such that r≥ and R≤c
3
C(d), where c
1,c
2,c
3 are constants that depend only on properties of the cones and the norms used. Therefore the condition measure C(d) is a relevant tool in proving the existence of an inscribed ball in the feasible region of P(d) that is not too far from the origin and whose radius is not too small.
Received November 2, 1995 / Revised version received June 26, 1998?Published online May 12, 1999 相似文献
9.
In this paper necessary, and sufficient optimality conditions are established without Lipschitz continuity for convex composite
continuous optimization model problems subject to inequality constraints. Necessary conditions for the special case of the
optimization model involving max-min constraints, which frequently arise in many engineering applications, are also given. Optimality conditions in the presence
of Lipschitz continuity are routinely obtained using chain rule formulas of the Clarke generalized Jacobian which is a bounded
set of matrices. However, the lack of derivative of a continuous map in the absence of Lipschitz continuity is often replaced
by a locally unbounded generalized Jacobian map for which the standard form of the chain rule formulas fails to hold. In this
paper we overcome this situation by constructing approximate Jacobians for the convex composite function involved in the model
problem using ε-perturbations of the subdifferential of the convex function and the flexible generalized calculus of unbounded
approximate Jacobians. Examples are discussed to illustrate the nature of the optimality conditions.
Received: February 2001 / Accepted: September 2001?Published online February 14, 2002 相似文献
10.