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1.
Received May 3, 1996 / Revised version received November 19, 1997 Published online January 20, 1999 相似文献
2.
Hesitant adaptive search is a stochastic optimisation procedure which accommodates hesitation, or pausing, at objective function
values. It lies between pure adaptive search (which strictly improves at each iteration) and simulated annealing with constant
temperature (which allows backtracking, or the acceptance of worse function values). In this paper we build on an earlier
work and make two contributions; first, we link hesitant adaptive search to standard counting process theory, and second,
we use this to derive the exact distribution of the number of iterations of hesitant adaptive search to termination.
Received: November 17, 1997 / Accepted: July 9, 1999?Published online December 15, 2000 相似文献
3.
Received April 15, 1997 / Revised version received July 22, 1998 Published online November 24, 1998 相似文献
4.
Received March 18, 1996 / Revised version received August 8, 1997 Published online November 24, 1998 相似文献
5.
Coercivity conditions and variational inequalities 总被引:9,自引:0,他引:9
Received November 17, 1997 / Revised version received August 6, 1998?Published online March 16, 1999 相似文献
6.
Received January 9, 1997 / Revised version received January 26, 1998
Published online November 24, 1998 相似文献
7.
This paper is about set packing relaxations of combinatorial optimization problems associated with acyclic digraphs and linear orderings, cuts and multicuts, and set
packings themselves. Families of inequalities that are valid for such a relaxation as well as the associated separation routines
carry over to the problems under investigation.
Received: September 1997 / Accepted: November 1999?Published online June 8, 2000 相似文献
8.
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
9.
Logarithmic SUMT limits in convex programming 总被引:1,自引:1,他引:0
The limits of a class of primal and dual solution trajectories associated with the Sequential Unconstrained Minimization Technique
(SUMT) are investigated for convex programming problems with non-unique optima. Logarithmic barrier terms are assumed. For
linear programming problems, such limits – of both primal and dual trajectories – are strongly optimal, strictly complementary,
and can be characterized as analytic centers of, loosely speaking, optimality regions. Examples are given, which show that
those results do not hold in general for convex programming problems. If the latter are weakly analytic (Bank et al. [3]),
primal trajectory limits can be characterized in analogy to the linear programming case and without assuming differentiability.
That class of programming problems contains faithfully convex, linear, and convex quadratic programming problems as strict
subsets. In the differential case, dual trajectory limits can be characterized similarly, albeit under different conditions,
one of which suffices for strict complementarity.
Received: November 13, 1997 / Accepted: February 17, 1999?Published online February 22, 2001 相似文献
10.
T (Mx+q)=0, Mx+q≥0, x≥0 has a solution. We explain how one can use the polyhedral structure of the set of all triangulations
of a finite point set to determine if an n×n matrix M is a Q-matrix. Our implementation of the algorithm is practical for
deciding the Q-nature for all M with n≤8.
Received May 30, 1997 / Revised version received June 12, 1998
Published online November 24, 1998 相似文献
11.
The 0-1 Knapsack problem with a single continuous variable 总被引:5,自引:0,他引:5
Specifically we investigate the polyhedral structure of the knapsack problem with a single continuous variable, called the
mixed 0-1 knapsack problem. First different classes of facet-defining inequalities are derived based on restriction and lifting. The order of
lifting, particularly of the continuous variable, plays an important role. Secondly we show that the flow cover inequalities
derived for the single node flow set, consisting of arc flows into and out of a single node with binary variable lower and
upper bounds on each arc, can be obtained from valid inequalities for the mixed 0-1 knapsack problem. Thus the separation
heuristic we derive for mixed knapsack sets can also be used to derive cuts for more general mixed 0-1 constraints. Initial
computational results on a variety of problems are presented.
Received May 22, 1997 / Revised version received December 22, 1997 Published online November 24, 1998 相似文献
12.
Feasible descent algorithms for mixed complementarity problems 总被引:6,自引:0,他引:6
In this paper we consider a general algorithmic framework for solving nonlinear mixed complementarity problems. The main features
of this framework are: (a) it is well-defined for an arbitrary mixed complementarity problem, (b) it generates only feasible
iterates, (c) it has a strong global convergence theory, and (d) it is locally fast convergent under standard regularity assumptions.
This framework is applied to the PATH solver in order to show viability of the approach. Numerical results for an appropriate
modification of the PATH solver indicate that this framework leads to substantial computational improvements.
Received April 9, 1998 / Revised version received November 23, 1998?Published online March 16, 1999 相似文献
13.
We prove that strict complementarity, primal and dual nondegeneracy of optimal solutions of convex optimization problems in
conic form are generic properties. In this paper, we say generic to mean that the set of data possessing the desired property
(or properties) has strictly larger Hausdorff dimension than the set of data that does not. Our proof is elementary and it
employs an important result due to Larman [7] on the boundary structure of convex bodies.
Received: September 1997 / Accepted: May 2000?Published online November 17, 2000 相似文献
14.
Received June 4, 1996 / Revised version received November 19, 1997
Published online November 24, 1998 相似文献
15.
The variational inequality problem (VIP) can be reformulated as an unconstrained minimization problem through the D-gap function.
It is proved that the D-gap function has bounded level sets for the strongly monotone VIP. A hybrid Newton-type method is
proposed for minimizing the D-gap function. Under some conditions, it is shown that the algorithm is globally convergent and
locally quadratically convergent.
Received May 6, 1997 / Revised version received October 30, 1998?Published online June 11, 1999 相似文献
16.
The concepts of L-convex function and M-convex function have recently been introduced by Murota as generalizations of submodular
function and base polyhedron, respectively, and discrete separation theorems are established for L-convex/concave functions
and for M-convex/concave functions as generalizations of Frank’s discrete separation theorem for submodular/supermodular set
functions and Edmonds’ matroid intersection theorem. This paper shows the equivalence between Murota’s L-convex functions
and Favati and Tardella’s submodular integrally convex functions, and also gives alternative proofs of the separation theorems
that provide a geometric insight by relating them to the ordinary separation theorem in convex analysis.
Received: November 27, 1997 / Accepted: December 16, 1999?Published online May 12, 2000 相似文献
17.
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
18.
Lagrangean dualization and subgradient optimization techniques are frequently used within the field of computational optimization
for finding approximate solutions to large, structured optimization problems. The dual subgradient scheme does not automatically
produce primal feasible solutions; there is an abundance of techniques for computing such solutions (via penalty functions,
tangential approximation schemes, or the solution of auxiliary primal programs), all of which require a fair amount of computational
effort.
We consider a subgradient optimization scheme applied to a Lagrangean dual formulation of a convex program, and construct,
at minor cost, an ergodic sequence of subproblem solutions which converges to the primal solution set. Numerical experiments
performed on a traffic equilibrium assignment problem under road pricing show that the computation of the ergodic sequence
results in a considerable improvement in the quality of the primal solutions obtained, compared to those generated in the
basic subgradient scheme.
Received February 11, 1997 / Revised version received June 19, 1998?Published online June 28, 1999 相似文献
19.
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
20.
The local quadratic convergence of the Gauss-Newton method for convex composite optimization f=h∘F is established for any convex function h with the minima set C, extending Burke and Ferris’ results in the case when C is a set of weak sharp minima for h.
Received: July 24, 1998 / Accepted: November 29, 2000?Published online September 3, 2001 相似文献