共查询到20条相似文献,搜索用时 31 毫秒
1.
Robust Optimization (RO) is a modeling methodology, combined with computational tools, to process optimization problems in
which the data are uncertain and is only known to belong to some uncertainty set. The paper surveys the main results of RO
as applied to uncertain linear, conic quadratic and semidefinite programming. For these cases, computationally tractable robust
counterparts of uncertain problems are explicitly obtained, or good approximations of these counterparts are proposed, making
RO a useful tool for real-world applications. We discuss some of these applications, specifically: antenna design, truss topology
design and stability analysis/synthesis in uncertain dynamic systems. We also describe a case study of 90 LPs from the NETLIB
collection. The study reveals that the feasibility properties of the usual solutions of real world LPs can be severely affected
by small perturbations of the data and that the RO methodology can be successfully used to overcome this phenomenon.
Received: May 24, 2000 / Accepted: September 12, 2001?Published online February 14, 2002 相似文献
2.
A branch and cut algorithm for nonconvex quadratically constrained quadratic programming 总被引:12,自引:0,他引:12
Charles Audet Pierre Hansen Brigitte Jaumard Gilles Savard 《Mathematical Programming》2000,87(1):131-152
We present a branch and cut algorithm that yields in finite time, a globally ε-optimal solution (with respect to feasibility
and optimality) of the nonconvex quadratically constrained quadratic programming problem. The idea is to estimate all quadratic
terms by successive linearizations within a branching tree using Reformulation-Linearization Techniques (RLT). To do so, four
classes of linearizations (cuts), depending on one to three parameters, are detailed. For each class, we show how to select
the best member with respect to a precise criterion. The cuts introduced at any node of the tree are valid in the whole tree,
and not only within the subtree rooted at that node. In order to enhance the computational speed, the structure created at
any node of the tree is flexible enough to be used at other nodes. Computational results are reported that include standard
test problems taken from the literature. Some of these problems are solved for the first time with a proof of global optimality.
Received December 19, 1997 / Revised version received July 26, 1999?Published online November 9, 1999 相似文献
3.
Krzysztof C. Kiwiel 《Mathematical Programming》2001,90(1):1-25
We study a general subgradient projection method for minimizing a quasiconvex objective subject to a convex set constraint
in a Hilbert space. Our setting is very general: the objective is only upper semicontinuous on its domain, which need not
be open, and various subdifferentials may be used. We extend previous results by proving convergence in objective values and
to the generalized solution set for classical stepsizes t
k
→0, ∑t
k
=∞, and weak or strong convergence of the iterates to a solution for {t
k
}∈ℓ2∖ℓ1 under mild regularity conditions. For bounded constraint sets and suitable stepsizes, the method finds ε-solutions with an
efficiency estimate of O(ε-2), thus being optimal in the sense of Nemirovskii.
Received: October 4, 1998 / Accepted: July 24, 2000?Published online January 17, 2001 相似文献
4.
Sensitivity analysis in linear programming and semidefinite programming using interior-point methods
We analyze perturbations of the right-hand side and the cost parameters in linear programming (LP) and semidefinite programming
(SDP). We obtain tight bounds on the perturbations that allow interior-point methods to recover feasible and near-optimal
solutions in a single interior-point iteration. For the unique, nondegenerate solution case in LP, we show that the bounds
obtained using interior-point methods compare nicely with the bounds arising from using the optimal basis. We also present
explicit bounds for SDP using the Monteiro-Zhang family of search directions and specialize them to the AHO, H..K..M, and
NT directions.
Received: December 1999 / Accepted: January 2001?Published online March 22, 2001 相似文献
5.
Francisco A. M. Gomes María Cristina Maciel José Mario Martínez 《Mathematical Programming》1999,84(1):161-200
The strategy for obtaining global convergence is based on the trust region approach. The merit function is a type of augmented
Lagrangian. A new updating scheme is introduced for the penalty parameter, by means of which monotone increase is not necessary.
Global convergence results are proved and numerical experiments are presented.
Received May 31, 1995 / Revised version received December 12, 1997 Published online October 21, 1998 相似文献
6.
We describe a new convex quadratic programming bound for the quadratic assignment problem (QAP). The construction of the bound
uses a semidefinite programming representation of a basic eigenvalue bound for QAP. The new bound dominates the well-known
projected eigenvalue bound, and appears to be competitive with existing bounds in the trade-off between bound quality and
computational effort.
Received: February 2000 / Accepted: November 2000?Published online January 17, 2001 相似文献
7.
Gongyun Zhao 《Mathematical Programming》2001,90(3):507-536
An algorithm incorporating the logarithmic barrier into the Benders decomposition technique is proposed for solving two-stage
stochastic programs. Basic properties concerning the existence and uniqueness of the solution and the underlying path are
studied. When applied to problems with a finite number of scenarios, the algorithm is shown to converge globally and to run
in polynomial-time.
Received: August 1998 / Accepted: August 2000?Published online April 12, 2001 相似文献
8.
Nonlinear programming without a penalty function 总被引:57,自引:0,他引:57
In this paper the solution of nonlinear programming problems by a Sequential Quadratic Programming (SQP) trust-region algorithm
is considered. The aim of the present work is to promote global convergence without the need to use a penalty function. Instead,
a new concept of a “filter” is introduced which allows a step to be accepted if it reduces either the objective function or
the constraint violation function. Numerical tests on a wide range of test problems are very encouraging and the new algorithm
compares favourably with LANCELOT and an implementation of Sl1QP.
Received: October 17, 1997 / Accepted: August 17, 2000?Published online September 3, 2001 相似文献
9.
Oktay Günlük 《Mathematical Programming》1999,86(1):17-39
We present a branch-and-cut algorithm to solve capacitated network design problems. Given a capacitated network and point-to-point
traffic demands, the objective is to install more capacity on the edges of the network and route traffic simultaneously, so
that the overall cost is minimized. We study a mixed-integer programming formulation of the problem and identify some new
facet defining inequalities. These inequalities, together with other known combinatorial and mixed-integer rounding inequalities,
are used as cutting planes. To choose the branching variable, we use a new rule called “knapsack branching”. We also report
on our computational experience using real-life data.
Received April 29, 1997 / Revised version received January 9, 1999? Published online June 28, 1999 相似文献
10.
Alan J. King 《Mathematical Programming》2002,91(3):543-562
The hedging of contingent claims in the discrete time, discrete state case is analyzed from the perspective of modeling the
hedging problem as a stochastic program. Application of conjugate duality leads to the arbitrage pricing theorems of financial
mathematics, namely the equivalence of absence of arbitrage and the existence of a probability measure that makes the price
process into a martingale. The model easily extends to the analysis of options pricing when modeling risk management concerns
and the impact of spreads and margin requirements for writers of contingent claims. However, we find that arbitrage pricing
in incomplete markets fails to model incentives to buy or sell options. An extension of the model to incorporate pre-existing
liabilities and endowments reveals the reasons why buyers and sellers trade in options. The model also indicates the importance
of financial equilibrium analysis for the understanding of options prices in incomplete markets.
Received: June 5, 2000 / Accepted: July 12, 2001?Published online December 6, 2001 相似文献
11.
Jörg Fliege 《Mathematical Programming》1999,84(2):435-438
Received September 3, 1997 / Revised version received March 20, 1998 Published online October 9, 1998 相似文献
12.
An improved rounding method and semidefinite programming relaxation for graph partition 总被引:8,自引:0,他引:8
Given an undirected graph G=(V,E) with |V|=n and an integer k between 0 and n, the maximization graph partition (MAX-GP) problem is to determine a subset S⊂V of k nodes such that an objective function w(S) is maximized. The MAX-GP problem can be formulated as a binary quadratic program and it is NP-hard. Semidefinite programming
(SDP) relaxations of such quadratic programs have been used to design approximation algorithms with guaranteed performance
ratios for various MAX-GP problems. Based on several earlier results, we present an improved rounding method using an SDP
relaxation, and establish improved approximation ratios for several MAX-GP problems, including Dense-Subgraph, Max-Cut, Max-Not-Cut,
and Max-Vertex-Cover.
Received: March 10, 2000 / Accepted: July 13, 2001?Published online February 14, 2002 相似文献
13.
The many facets of linear programming 总被引:1,自引:0,他引:1
Michael J. Todd 《Mathematical Programming》2002,91(3):417-436
We examine the history of linear programming from computational, geometric, and complexity points of view, looking at simplex,
ellipsoid, interior-point, and other methods.
Received: June 22, 2000 / Accepted: April 4, 2001?Published online October 2, 2001 相似文献
14.
Global and polynomial-time convergence of an infeasible-interior-point algorithm using inexact computation 总被引:2,自引:0,他引:2
Received April 10, 1996 / Revised version received April 30, 1998 Published online August 18, 1998 相似文献
15.
Robustness of posynomial geometric programming optima 总被引:3,自引:0,他引:3
Received April 2, 1998 / Revised version received July 8, 1998
Published online November 24, 1998 相似文献
16.
Interior-point methods for nonconvex nonlinear programming: orderings and higher-order methods 总被引:6,自引:0,他引:6
The paper extends prior work by the authors on loqo, an interior point algorithm for nonconvex nonlinear programming. The
specific topics covered include primal versus dual orderings and higher order methods, which attempt to use each factorization
of the Hessian matrix more than once to improve computational efficiency. Results show that unlike linear and convex quadratic
programming, higher order corrections to the central trajectory are not useful for nonconvex nonlinear programming, but that
a variant of Mehrotra’s predictor-corrector algorithm can definitely improve performance.
Received: May 3, 1999 / Accepted: January 24, 2000?Published online March 15, 2000 相似文献
17.
In this paper, we introduce a transformation that converts a class of linear and nonlinear semidefinite programming (SDP)
problems into nonlinear optimization problems. For those problems of interest, the transformation replaces matrix-valued constraints
by vector-valued ones, hence reducing the number of constraints by an order of magnitude. The class of transformable problems
includes instances of SDP relaxations of combinatorial optimization problems with binary variables as well as other important
SDP problems. We also derive gradient formulas for the objective function of the resulting nonlinear optimization problem
and show that both function and gradient evaluations have affordable complexities that effectively exploit the sparsity of
the problem data. This transformation, together with the efficient gradient formulas, enables the solution of very large-scale
SDP problems by gradient-based nonlinear optimization techniques. In particular, we propose a first-order log-barrier method
designed for solving a class of large-scale linear SDP problems. This algorithm operates entirely within the space of the
transformed problem while still maintaining close ties with both the primal and the dual of the original SDP problem. Global
convergence of the algorithm is established under mild and reasonable assumptions.
Received: January 5, 2000 / Accepted: October 2001?Published online February 14, 2002 相似文献
18.
19.
In a recent paper, the authors have proved results characterizing convexity-preserving maps defined on a subset of a not-necessarily
finite dimensional real vector space as projective maps. The purpose of this note is three-fold. First, we state a theorem
characterizing continuous, injective, convexity-preserving maps from a relatively open, connected subset of an affine subspace
of ℝ
m
into ℝ
n
as projective maps. This result follows from the more general results stated and proved in a coordinate-free manner in the
above paper, and is intended to be more accessible to researchers interested in optimization algorithms. Second, based on
that characterization theorem, we offer a characterization theorem for collinear scalings first introduced by Davidon in 1977
for deriving certain algorithms for nonlinear optimization, and a characterization theorem for projective transformations
used by Karmarkar in 1984 in his linear programming algorithm. These latter two theorems indicate that Davidon’s collinear
scalings and Karmarkar’s projective transformations are the only continuous, injective, convexity-preserving maps possessing
certain features that Davidon and Karmarkar respectively desired in the derivation of their algorithms. The proofs of these
latter two theorems utilize our characterization of continuous, injective, convexity-preserving maps in a way that has implications
to the choice of scalings and transformations in the derivation of optimization algorithms in general. The third purpose of
this note is to point this out.
Received: January 2000 / Accepted: November 2000?Published online January 17, 2001 相似文献
20.
Computational study of a column generation algorithm for bin packing and cutting stock problems 总被引:4,自引:0,他引:4
François Vanderbeck 《Mathematical Programming》1999,86(3):565-594
This paper reports on our attempt to design an efficient exact algorithm based on column generation for the cutting stock
problem. The main focus of the research is to study the extend to which standard branch-and-bound enhancement features such
as variable fixing, the tightening of the formulation with cutting planes, early branching, and rounding heuristics can be
usefully incorporated in a branch-and-price algorithm. We review and compare lower bounds for the cutting stock problem. We
propose a pseudo-polynomial heuristic. We discuss the implementation of the important features of the integer programming
column generation algorithm and, in particular, the implementation of the branching scheme. Our computational results demonstrate
the efficiency of the resulting algorithm for various classes of bin packing and cutting stock problems.
Received October 18, 1996 / Revised version received May 14, 1998?Published online July 19, 1999 相似文献