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2.
On cone characterizations of weak, proper and Pareto optimality in multiobjective optimization 总被引:1,自引:0,他引:1
Efficient, weakly and properly Pareto optimal solutions of multiobjective optimization problems can be characterized with the help of different cones. Here, contingent, tangent and normal cones as well as cones of feasible directions are used in the characterizations. The results are first presented in convex cases and then generalized to nonconvex cases by employing local concepts. 相似文献
3.
R. Kasimbeyli 《Nonlinear Analysis: Theory, Methods & Applications》2011,74(7):2534-2547
In this paper we study optimality conditions for optimization problems described by a special class of directionally differentiable functions. The well-known necessary and sufficient optimality condition of nonsmooth convex optimization, given in the form of variational inequality, is generalized to the nonconvex case by using the notion of weak subdifferentials. The equivalent formulation of this condition in terms of weak subdifferentials and augmented normal cones is also presented. 相似文献
4.
Lagrangian relaxation is often an efficient tool to solve (large-scale) optimization problems, even nonconvex. However it
introduces a duality gap, which should be small for the method to be really efficient. Here we make a geometric study of the
duality gap. Given a nonconvex problem, we formulate in a first part a convex problem having the same dual. This formulation
involves a convexification in the product of the three spaces containing respectively the variables, the objective and the
constraints. We apply our results to several relaxation schemes, especially one called “Lagrangean decomposition” in the combinatorial-optimization
community, or “operator splitting” elsewhere. We also study a specific application, highly nonlinear: the unit-commitment
problem.
Received: June 1997 / Accepted: December 2000?Published online April 12, 2001 相似文献
5.
The strong conical hull intersection property and bounded linear regularity are properties of a collection of finitely many
closed convex intersecting sets in Euclidean space. These fundamental notions occur in various branches of convex optimization
(constrained approximation, convex feasibility problems, linear inequalities, for instance). It is shown that the standard
constraint qualification from convex analysis implies bounded linear regularity, which in turn yields the strong conical hull
intersection property. Jameson’s duality for two cones, which relates bounded linear regularity to property (G), is re-derived
and refined. For polyhedral cones, a statement dual to Hoffman’s error bound result is obtained. A sharpening of a result
on error bounds for convex inequalities by Auslender and Crouzeix is presented. Finally, for two subspaces, property (G) is
quantified by the angle between the subspaces.
Received October 1, 1997 / Revised version received July 21, 1998? Published online June 11, 1999 相似文献
6.
We generalize the disjunctive approach of Balas, Ceria, and Cornuéjols [2] and devevlop a branch-and-cut method for solving
0-1 convex programming problems. We show that cuts can be generated by solving a single convex program. We show how to construct
regions similar to those of Sherali and Adams [20] and Lovász and Schrijver [12] for the convex case. Finally, we give some
preliminary computational results for our method.
Received January 16, 1996 / Revised version received April 23, 1999?Published online June 28, 1999 相似文献
7.
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
8.
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
9.
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
10.
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
11.
An interior Newton method for quadratic programming 总被引:2,自引:0,他引:2
We propose a new (interior) approach for the general quadratic programming problem. We establish that the new method has strong
convergence properties: the generated sequence converges globally to a point satisfying the second-order necessary optimality
conditions, and the rate of convergence is 2-step quadratic if the limit point is a strong local minimizer. Published alternative
interior approaches do not share such strong convergence properties for the nonconvex case. We also report on the results
of preliminary numerical experiments: the results indicate that the proposed method has considerable practical potential.
Received October 11, 1993 / Revised version received February 20, 1996
Published online July 19, 1999 相似文献
12.
Martin Gugat 《Mathematical Programming》2000,88(2):255-275
The feasible set of a convex semi–infinite program is described by a possibly infinite system of convex inequality constraints.
We want to obtain an upper bound for the distance of a given point from this set in terms of a constant multiplied by the
value of the maximally violated constraint function in this point. Apart from this Lipschitz case we also consider error bounds
of H?lder type, where the value of the residual of the constraints is raised to a certain power.?We give sufficient conditions
for the validity of such bounds. Our conditions do not require that the Slater condition is valid. For the definition of our
conditions, we consider the projections on enlarged sets corresponding to relaxed constraints. We present a condition in terms
of projection multipliers, a condition in terms of Slater points and a condition in terms of descent directions. For the Lipschitz
case, we give five equivalent characterizations of the validity of a global error bound.?We extend previous results in two
directions: First, we consider infinite systems of inequalities instead of finite systems. The second point is that we do
not assume that the Slater condition holds which has been required in almost all earlier papers.
Received: April 12, 1999 / Accepted: April 5, 2000?Published online July 20, 2000 相似文献
13.
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
14.
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
15.
Solving large quadratic assignment problems on computational grids 总被引:10,自引:0,他引:10
Kurt Anstreicher Nathan Brixius Jean-Pierre Goux Jeff Linderoth 《Mathematical Programming》2002,91(3):563-588
The quadratic assignment problem (QAP) is among the hardest combinatorial optimization problems. Some instances of size n = 30 have remained unsolved for decades. The solution of these problems requires both improvements in mathematical programming
algorithms and the utilization of powerful computational platforms. In this article we describe a novel approach to solve
QAPs using a state-of-the-art branch-and-bound algorithm running on a federation of geographically distributed resources known
as a computational grid. Solution of QAPs of unprecedented complexity, including the nug30, kra30b, and tho30 instances, is
reported.
Received: September 29, 2000 / Accepted: June 5, 2001?Published online October 2, 2001 相似文献
16.
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
17.
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
18.
In this paper, we consider a special class of nonconvex programming problems for which the objective function and constraints
are defined in terms of general nonconvex factorable functions. We propose a branch-and-bound approach based on linear programming
relaxations generated through various approximation schemes that utilize, for example, the Mean-Value Theorem and Chebyshev
interpolation polynomials coordinated with a Reformulation-Linearization Technique (RLT). A suitable partitioning process
is proposed that induces convergence to a global optimum. The algorithm has been implemented in C++ and some preliminary computational
results are reported on a set of fifteen engineering process control and design test problems from various sources in the
literature. The results indicate that the proposed procedure generates tight relaxations, even via the initial node linear
program itself. Furthermore, for nine of these fifteen problems, the application of a local search method that is initialized
at the LP relaxation solution produced the actual global optimum at the initial node of the enumeration tree. Moreover, for
two test cases, the global optimum found improves upon the solutions previously reported in the source literature.
Received: January 14, 1998 / Accepted: June 7, 1999?Published online December 15, 2000 相似文献
19.
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
20.
《Optimization》2012,61(3):283-304
Given a convex vector optimization problem with respect to a closed ordering cone, we show the connectedness of the efficient and properly efficient sets. The Arrow–Barankin–Blackwell theorem is generalized to nonconvex vector optimization problems, and the connectedness results are extended to convex transformable vector optimization problems. In particular, we show the connectedness of the efficient set if the target function f is continuously transformable, and of the properly efficient set if f is differentiably transformable. Moreover, we show the connectedness of the efficient and properly efficient sets for quadratic quasiconvex multicriteria optimization problems. 相似文献