Two–parameter scaled memoryless BFGS methods with a nonmonotone choice for the initial step length

Numerical Algorithms - A class of two–parameter scaled memoryless BFGS methods is developed for solving unconstrained optimization problems. Then, the scaling parameters are determined in a...     

Decomposition methods for polyhedral approximation of the Edgeworth–Pareto hull

In the framework of multiobjective optimization (MOO) techniques, there is a group of methods that attract growing attention. These methods are based on the approximation of the Edgeworth–Pareto hull (EPH), that is, the largest set (in the sense of inclusion) that has the same Pareto frontier as the feasible objective set [1, 2]. The block separable structure of MOO problems is used in this paper to improve the efficiency of EPH approximation methods in the case when EPH is convex. In this case, polyhedral approximation can be applied to EPH. Its practical importance was shown, for example, in [3, 4]. In this paper, a two-level system is considered that consists of an upper (coordinating) level and a finite number of blocks interacting through the upper level. It is assumed that the optimization objectives are related only to the variables of the upper level. The approximation method is based on the polyhedral approximation of EPH for single blocks, followed by the application of these results for approximating the EPH for the whole MOO problem.     

Adaptive methods for solvings minimax problems ∗

We consider finite-dimensional minimax problems for two traditional models: firstly,with box constraints at variables and,secondly,taking into account a finite number of tinear inequalities. We present finite exact primal and dual methods. These methods are adapted to a great extent to the specific structure of the cost function which is formed by a finite number of linear functions. During the iterations of the primal method we make use of the information from the dual problem, thereby increasing effectiveness. To improve the dual method we use the “long dual step” rule (the principle of ullrelaxation).The results are illustrated by numerical experiments.     

Modified Runge–Kutta Verner methods for the numerical solution of initial and boundary-value problems with engineering applications

Two new modified Runge–Kutta methods with minimal phase-lag are developed for the numerical solution of Ordinary Differential Equations with engineering applications. These methods are based on the well-known Runge–Kutta method of Verner RK6(5)9b (see J.H. Verner, some Runge–Kutta formula pairs, SIAM J. Numer. Anal 28 (1991) 496–511) of order six. Numerical and theoretical results in some problems of the plate deflection theory show that this new approach is more efficient compared with the well-known classical sixth order Runge–Kutta Verner method.     

On the continuity of the minima for a family of constrained optimization problems ∗

We consider a family of problems Py dealing with the minimization of a given function on a constraint set, both depending on a parameter y. We study continuity properties, with respect to a parameter, of the value and of the solution set of the problems. Working with convex functions and convex constraint sets, we show how the well-posedness of the problem allows to avoid compactness hypotheses usually requested to get the same stability results.     

An approximation for the nonlinear filtering problem,with error bound †

For the standard continuous-time nonlinear filtering problem an approximation approach is derived. The approximate filter is given by the solution to an appropriate discrete-time approximating filtering problem that can be explicitly solved by a finite-dimensional procedure. Furthermore an explicit upper bound for the approximation error is derived. The approximating problem is obtained by first approximating the signal and then using measure transformation to express the original observation process in terms of the approximating signal     

A conditionally almost linear filtering problem with non-gaussian initial condition ∗

We derive a formula for computing explicitly the optimal non-linear filter for a class of problems which admit finite dimensional filters. The result includes all known results of this kind as special cases     

Nonadaptive methods for polyhedral approximation of the Edgeworth—Pareto hull using suboptimal coverings on the direction sphere

For multicriteria convex optimization problems, new nonadaptive methods are proposed for polyhedral approximation of the multidimensional Edgeworth-Pareto hull (EPH), which is a maximal set having the same Pareto frontier as the set of feasible criteria vectors. The methods are based on evaluating the support function of the EPH for a collection of directions generated by a suboptimal covering on the unit sphere. Such directions are constructed in advance by applying an asymptotically effective adaptive method for the polyhedral approximation of convex compact bodies, namely, by the estimate refinement method. Due to the a priori definition of the directions, the proposed EPH approximation procedure can easily be implemented with parallel computations. Moreover, the use of nonadaptive methods considerably simplifies the organization of EPH approximation on the Internet. Experiments with an applied problem (from 3 to 5 criteria) showed that the methods are fairly similar in characteristics to adaptive methods. Therefore, they can be used in parallel computations and on the Internet.     

Stability and integrability aspects for the Maxwell–Bloch equations with the rotating wave approximation

Infinitely many Hamilton–Poisson realizations of the five-dimensional real valued Maxwell–Bloch equations with the rotating wave approximation are constructed and the energy-Casimir mapping is considered. Also, the image of this mapping is presented and connections with the equilibrium states of the considered system are studied. Using some fibers of the image of the energy-Casimir mapping, some special orbits are obtained. Finally, a Lax formulation of the system is given.     

On the stability of some second order numerical methods for weak approximation of Itô SDEs

In this paper, we first investigate the stability of two weak second order methods introduced by Debrabant and Rößler (Appl Numer Math 59:582–594, 2009) and Platen (Math Comput Simulation 38:69–76, 1995). We then propose a new weak second order predictor-corrector method, with an improved stability properties, based on the Rößler’s method as the predictor and the implicit method of Platen as the corrector. The stability functions of these methods, applied to a scalar linear test equation with multiplicative noise, are determined and their regions of stability are then compared with the corresponding stability regions of the test equation. Furthermore, we also investigate mean square stability (MS-stability) of these methods applied to a linear Itô 2-dimensional stochastic differential test equation. Numerical examples will be presented to support the theoretical results.     

On the Cauchy problem for the generalized Benjamin–Ono equation with small initial data

We prove global well-posedness results for small initial data in $\text{H}{}^{\mathrm{s}}\text{(}\text{R}\text{),}\text{s>s}{}_{\mathrm{k}}$, and in , s_k=1/2?1/k, for the generalized Benjamin–Ono equation $\text{?}{}_{\mathrm{t}}\text{u+}\text{H}\text{?}{}^2{}_{\mathrm{x}}\text{u+?}{}_{\mathrm{x}}\text{(u}{}^{\mathrm{k+1}}\text{)=0,}\text{k?4}$. We also consider the cases k=2,3. To cite this article: L. Molinet, F. Ribaud, C. R. Acad. Sci. Paris, Ser. I 337 (2003).     

Strong convergence for explicit space–time discrete numerical approximation methods for stochastic Burgers equations

In this paper we propose and analyze explicit space–time discrete numerical approximations for additive space–time white noise driven stochastic partial differential equations (SPDEs) with non-globally monotone nonlinearities such as the stochastic Burgers equation with space–time white noise. The main result of this paper proves that the proposed explicit space–time discrete approximation method converges strongly to the solution process of the stochastic Burgers equation with space–time white noise. To the best of our knowledge, the main result of this work is the first result in the literature which establishes strong convergence for a space–time discrete approximation method in the case of the stochastic Burgers equations with space–time white noise.     

Laplace transform–homotopy perturbation method with arbitrary initial approximation and residual error cancelation

This paper presents a modified Laplace transform homotopy perturbation method with finite boundary conditions (MLT–HPM) designed to improve the accuracy of the approximate solutions obtained by LT–HPM and other methods. To this purpose, a suitable initial approximation will be introduced, in addition, the residual error in several points of the interest interval (RECP) will be canceled. In order to prove the efficiency of the proposed method a couple of nonlinear ordinary differential equations with mixed boundary conditions, indeed, difficult to approximate, are proposed. The square residual error (S.R.E) of the proposed solutions will result to be of hundredths and tenths, requiring only a first order approximation of MLT–HPM, unlike LT–HPM, which will require more iterations for the same cases study.     

A new family of Runge–Kutta type methods for the numerical integration of perturbed oscillators

Summary. Our task in this paper is to present a new family of methods of the Runge–Kutta type for the numerical integration of perturbed oscillators. The key property is that those algorithms are able to integrate exactly, without truncation error, harmonic oscillators, and that, for perturbed problems the local error contains the perturbation parameter as a factor. Some numerical examples show the excellent behaviour when they compete with Runge–Kutta–Nystr?m type methods. Received June 12, 1997 / Revised version received July 9, 1998     

A family of hybrid exponentially fitted predictor-corrector methods for the numerical integration of the radial Schrödinger equation

A family of hybrid, exponentially fitted, predictor-corrector methods is developed for the numerical integration of the one-dimensional Schrödinger equation. The formula considered contains certain free parameters which allow it to be fitted automatically to exponential functions. The new methods are of algebraic order six, they are very simple and integrate more exponential functions than both the well-known fourth-order Numerov-type exponentially fitted methods and the Runge-Kutta-type methods of algebraic order six. Numerical results also indicate that the new methods are much more accurate than the other exponentially fitted methods mentioned above.     

Uniqueness for the two-dimensional Navier–Stokes equation with a measure as initial vorticity

We show that any solution of the two-dimensional Navier-Stokes equation whose vorticity distribution is uniformly bounded in L¹(R²) for positive times is entirely determined by the trace of the vorticity at t=0, which is a finite measure. When combined with previous existence results by Cottet, by Giga, Miyakawa & Osada, and by Kato, this uniqueness property implies that the Cauchy problem for the vorticity equation in R² is globally well-posed in the space of finite measures. In particular, this provides an example of a situation where the Navier-Stokes equation is well-posed for arbitrary data in a function space that is large enough to contain the initial data of some self-similar solutions.     

Some non-interior path-following methods based on a scaled central path for linear complementarity problems

In this paper we present some non-interior path-following methods for linear complementarity problems. Instead of using the standard central path we use a scaled central path. Based on this new central path, we first give a feasible non-interior path-following method for linear complementarity problems. And then we extend it to an infeasible method. After proving the boundedness of the neighborhood, we prove the convergence of our method. Another point we should present is that we prove the local quadratic convergence of feasible method without the assumption of strict complementarity at the solution.     

Saddle point methods,and alogorithms,for non-symmetric linear equations ∗

This paper describes methods for solving non-singular, non-symmetric linear equations whose symmetric part is positive definite. First, the solutions are characterized as saddle points of a convex-concave function. The associated primal and dual variational principles provide quadratic, strictly convex, functions whose minima are the solutions of the original equation and which generalize the energy function for symmetric problems.

Direct iterative methods for finding the saddle point are then developed and analyzed. A globally convergent algorithm for finding the saddle points is described. We show that requiring conjugacy of successive search directions with respect to the symmetric part of the equation is a poor strategy.     

Linear information for approximation of the Itô integrals

We study optimal approximation of stochastic integrals in the Itô sense when linear information, consisting of certain integrals of trajectories of Brownian motion, is available. Upper bounds on the nth minimal error, where n is the fixed cardinality of information, are obtained by the Wagner–Platen algorithm and are O(n ^???3/2) or O(n ^???2), depending on considered class of integrands. We also show that Ω(n ^???2) is a lower bound which holds even for very smooth integrands.