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31.
We propose an analog model for quantum gravity effects using nonlinear dielectrics. Fluctuations of the spacetime lightcone are expected in quantum gravity, leading to variations in the flight times of pulses. This effect can also arise in a nonlinear material. We propose a model in which fluctuations of a background electric field, such as that produced by a squeezed photon state, can cause fluctuations in the effective lightcone for probe pulses. This leads to a variation in flight times analogous to that in quantum gravity. We make some numerical estimates which suggest that the effect might be large enough to be observable. 相似文献
32.
Given a maximal monotone operator T in a Banach space, we consider an enlargement T, in which monotonicity is lost up to , in a very similar way to the -subdifferential of a convex function. We establish in this general framework some theoretical properties of T, like a transportation formula, local Lipschitz continuity, local boundedness, and a Brøndsted–Rockafellar property. 相似文献
33.
34.
Error bounds for proximal point subproblems and associated inexact proximal point algorithms 总被引:1,自引:0,他引:1
We study various error measures for approximate solution of proximal point regularizations of the variational inequality problem,
and of the closely related problem of finding a zero of a maximal monotone operator. A new merit function is proposed for
proximal point subproblems associated with the latter. This merit function is based on Burachik-Iusem-Svaiter’s concept of
ε-enlargement of a maximal monotone operator. For variational inequalities, we establish a precise relationship between the
regularized gap function, which is a natural error measure in this context, and our new merit function. Some error bounds
are derived using both merit functions for the corresponding formulations of the proximal subproblem. We further use the regularized
gap function to devise a new inexact proximal point algorithm for solving monotone variational inequalities. This inexact
proximal point method preserves all the desirable global and local convergence properties of the classical exact/inexact method,
while providing a constructive error tolerance criterion, suitable for further practical applications. The use of other tolerance
rules is also discussed.
Received: April 28, 1999 / Accepted: March 24, 2000?Published online July 20, 2000 相似文献
35.
This paper concerns with convergence properties of the classical proximal point algorithm for finding zeroes of maximal monotone
operators in an infinite-dimensional Hilbert space. It is well known that the proximal point algorithm converges weakly to
a solution under very mild assumptions. However, it was shown by Güler [11] that the iterates may fail to converge strongly
in the infinite-dimensional case. We propose a new proximal-type algorithm which does converge strongly, provided the problem
has a solution. Moreover, our algorithm solves proximal point subproblems inexactly, with a constructive stopping criterion
introduced in [31]. Strong convergence is forced by combining proximal point iterations with simple projection steps onto
intersection of two halfspaces containing the solution set. Additional cost of this extra projection step is essentially negligible
since it amounts, at most, to solving a linear system of two equations in two unknowns.
Received January 6, 1998 / Revised version received August 9, 1999?Published online November 30, 1999 相似文献
36.
We propose and study the iteration-complexity of a proximal-Newton method for finding approximate solutions of the problem of minimizing a twice continuously differentiable convex function on a (possibly infinite dimensional) Hilbert space. We prove global convergence rates for obtaining approximate solutions in terms of function/gradient values. Our main results follow from an iteration-complexity study of an (large-step) inexact proximal point method for solving convex minimization problems. 相似文献
37.
We prove Kantorovich’s theorem on Newton’s method using a convergence analysis which makes clear, with respect to Newton’s
method, the relationship of the majorant function and the non-linear operator under consideration. This approach enables us
to drop out the assumption of existence of a second root for the majorant function, still guaranteeing Q-quadratic convergence rate and to obtain a new estimate of this rate based on a directional derivative of the derivative of the majorant function. Moreover, the majorant function does not have to be defined beyond its first root for obtaining
convergence rate results.
The research of O.P. Ferreira was supported in part by FUNAPE/UFG, CNPq Grant 475647/2006-8, CNPq Grant 302618/2005-8, PRONEX–Optimization(FAPERJ/CNPq)
and IMPA.
The research of B.F. Svaiter was supported in part by CNPq Grant 301200/93-9(RN) and by PRONEX–Optimization(FAPERJ/CNPq). 相似文献
38.
A first-order block-decomposition method for solving two-easy-block structured semidefinite programs
Renato D. C. Monteiro Camilo Ortiz Benar F. Svaiter 《Mathematical Programming Computation》2014,6(2):103-150
In this paper, we consider a first-order block-decomposition method for minimizing the sum of a convex differentiable function with Lipschitz continuous gradient, and two other proper closed convex (possibly, nonsmooth) functions with easily computable resolvents. The method presented contains two important ingredients from a computational point of view, namely: an adaptive choice of stepsize for performing an extragradient step; and the use of a scaling factor to balance the blocks. We then specialize the method to the context of conic semidefinite programming (SDP) problems consisting of two easy blocks of constraints. Without putting them in standard form, we show that four important classes of graph-related conic SDP problems automatically possess the above two-easy-block structure, namely: SDPs for $\theta $ -functions and $\theta _{+}$ -functions of graph stable set problems, and SDP relaxations of binary integer quadratic and frequency assignment problems. Finally, we present computational results on the aforementioned classes of SDPs showing that our method outperforms the three most competitive codes for large-scale conic semidefinite programs, namely: the boundary point (BP) method introduced by Povh et al., a Newton-CG augmented Lagrangian method, called SDPNAL, by Zhao et al., and a variant of the BP method, called the SPDAD method, by Wen et al. 相似文献
39.
Descent methods with linesearch in the presence of perturbations 总被引:3,自引:0,他引:3
We consider the class of descent algorithms for unconstrained optimization with an Armijo-type stepsize rule in the case when the gradient of the objective function is computed inexactly. An important novel feature in our theoretical analysis is that perturbations associated with the gradient are not assumed to be relatively small or to tend to zero in the limit (as a practical matter, we expect them to be reasonably small, so that a meaningful approximate solution can be obtained). This feature makes our analysis applicable to various difficult problems encounted in practice. We propose a modified Armijo-type rule for computing the stepsize which guarantees that the algorithm obtains a reasonable approximate solution. Furthermore, if perturbations are small relative to the size of the gradient, then our algorithm retains all the standard convergence properties of descent methods. 相似文献
40.
A new optimality condition for minimization with general constraints is introduced. Unlike the KKT conditions, the new condition is satisfied by local minimizers of nonlinear programming problems, independently of constraint qualifications. The new condition is strictly stronger than and implies the Fritz–John optimality conditions. Sufficiency for convex programming is proved. 相似文献