非Fuchs型方程的新理论——树级数解的表现定理(Ⅰ)

在微分方程的解析理论中非Fuchs型方程的严格显式解至今并未求得(Poincaré问题),本文提出的新理论首次给出非正则积分的一般求法和显式的精确解. 本法与经典理论的根本不同在于摈弃形式解的假定,从方程本身建立对应关系,应用留数定理自动给出非正则积分的解析结构.它由无收缩部和全、半收缩部组成.前者是通常的递推级数,后者则表为树级数.树级数是类新颖的解析函数,通常的递推级数只是它的特例而已. 本文的目的是建立非正则积分的一般理论,为此需要阐明Poincaré问题(1880T.I.P.333)的实质[1]:无法求出非正则积分的显式.根据以下证明的表现定理, 非正则积分是类新颖的解析函数,其中系数D_nk是方程参数的常项树级数.     

On modified method of simplest equation for obtaining exact and approximate solutions of nonlinear PDEs: The role of the simplest equation

The modified method of simplest equation is powerful tool for obtaining exact and approximate solutions of nonlinear PDEs. These solutions are constructed on the basis of solutions of more simple equations called simplest equations. In this paper we study the role of the simplest equation for the application of the modified method of simplest equation. We follow the idea that each function constructed as polynomial of a solution of a simplest equation is a solution of a class of nonlinear PDEs. We discuss three simplest equations: the equations of Bernoulli and Riccati and the elliptic equation. The applied algorithm is as follows. First a polynomial function is constructed on the basis of a simplest equation. Then we find nonlinear ODEs that have the constructed function as a particular solution. Finally we obtain nonlinear PDEs that by means of the traveling-wave ansatz can be reduced to the above ODEs. By means of this algorithm we make a first step towards identification of the above-mentioned classes of nonlinear PDEs.     

Likelihood robust optimization for data-driven problems

We consider optimal decision-making problems in an uncertain environment. In particular, we consider the case in which the distribution of the input is unknown, yet there is some historical data drawn from the distribution. In this paper, we propose a new type of distributionally robust optimization model called the likelihood robust optimization (LRO) model for this class of problems. In contrast to previous work on distributionally robust optimization that focuses on certain parameters (e.g., mean, variance, etc.) of the input distribution, we exploit the historical data and define the accessible distribution set to contain only those distributions that make the observed data achieve a certain level of likelihood. Then we formulate the targeting problem as one of optimizing the expected value of the objective function under the worst-case distribution in that set. Our model avoids the over-conservativeness of some prior robust approaches by ruling out unrealistic distributions while maintaining robustness of the solution for any statistically likely outcomes. We present statistical analyses of our model using Bayesian statistics and empirical likelihood theory. Specifically, we prove the asymptotic behavior of our distribution set and establish the relationship between our model and other distributionally robust models. To test the performance of our model, we apply it to the newsvendor problem and the portfolio selection problem. The test results show that the solutions of our model indeed have desirable performance.     

Multivariate stable distributions and generating densities

The probability density function of multivariate stable distributions only applies to special accessible cases. Consequently, because of the absence of an explicit solution for their probability distribution function, applications have been limited. In this paper, we present an analytic method for generating densities to resolve this problem. Some examples and special cases are discussed.     

Wireless three-hop networks with stealing II: exact solutions through boundary value problems

We study the stationary distribution of a random walk in the quarter plane arising in the study of three-hop wireless networks with stealing. Our motivation is to find exact tail asymptotics (beyond logarithmic estimates) for the marginal distributions, which requires an exact solution for the bivariate generating function describing the stationary distribution. This exact solution is determined via the theory of boundary value problems. Although this is a classical approach, the present random walk exhibits some salient features. In fact, to determine the exact tail asymptotics, the random walk presents several unprecedented challenges related to conformal mappings and analytic continuation. We address these challenges by formulating a boundary value problem different from the one usually seen in the literature.     

A Class of Solutions for the Equations Governing the Steady Two-Dimensional Motion of a Viscous Incompressible Liquid

A general viscosity dependent solution for the stream function is found satisfying the simplest nondegenerate form of the steady flow Navier-Stokes equations for a viscous incompressible liquid. The solution is two-dimensional and is expressed in terms of arbitrary analytic functions in the fluid domain. This class of flows is generated by complex stream functions, and the region of definition is restricted by an inequality containing these analytic functions. A general potential flow, and degenerate Stokes or creeping flows are recovered as particular solutions in limiting cases.     

Multiexponential models of (1+1)-dimensional dilaton gravity and Toda-Liouville integrable models

We study general properties of a class of two-dimensional dilaton gravity (DG) theories with potentials containing several exponential terms. We isolate and thoroughly study a subclass of such theories in which the equations of motion reduce to Toda and Liouville equations. We show that the equation parameters must satisfy a certain constraint, which we find and solve for the most general multiexponential model. It follows from the constraint that integrable Toda equations in DG theories generally cannot appear without accompanying Liouville equations. The most difficult problem in the two-dimensional Toda-Liouville (TL) DG is to solve the energy and momentum constraints. We discuss this problem using the simplest examples and identify the main obstacles to solving it analytically. We then consider a subclass of integrable two-dimensional theories where scalar matter fields satisfy the Toda equations and the two-dimensional metric is trivial. We consider the simplest case in some detail. In this example, we show how to obtain the general solution. We also show how to simply derive wavelike solutions of general TL systems. In the DG theory, these solutions describe nonlinear waves coupled to gravity and also static states and cosmologies. For static states and cosmologies, we propose and study a more general one-dimensional TL model typically emerging in one-dimensional reductions of higher-dimensional gravity and supergravity theories. We especially attend to making the analytic structure of the solutions of the Toda equations as simple and transparent as possible.     

On a self-similar solution of a two-dimensional filtration problem in regions with moving boundaries : PMM vol. 38, n 6, 1974, pp. 1136–1139

We determine a family of self-similar solutions of a two-dimensional problem involving the filtration of an incompressible liquid in regions with moving boundaries. Our work is based on a method developed by Galin for solving the problem of settling of water cones in a gravitational field [1 – 3]. Following this method, we reduce the problem to one of finding an analytic function of a complex variable and the time, which effects a conformal mapping of the filtration region onto a strip and satisfies a special nonlinear condition on the boundary. For the solution of a problem of this kind Galin proposed the method of successive approximations.     

Research topics in discrete estimation of distribution algorithms based on factorizations

In this paper, we identify a number of topics relevant for the improvement and development of discrete estimation of distribution algorithms. Focusing on the role of probability distributions and factorizations in estimation of distribution algorithms, we present a survey of current challenges where further research must provide answers that extend the potential and applicability of these algorithms. In each case we state the research topic and elaborate on the reasons that make it relevant for estimation of distribution algorithms. In some cases current work or possible alternatives for the solution of the problem are discussed.     

Dirichlet problem in the ball for an equation with a delta-like coefficient

We study the Dirichlet problem in the ball for the Helmholtz equation with an additional term in the form of the product by the delta function. The additional term is approximated by the simplest integral expression, and the solution of the original equation is defined as the limit of solutions of the regularized equations. We obtain exact solutions depending on the chosen method of approximation.     

Hyperfunctions and (analytic) hypoellipticity

In this work we discuss the problem of smooth and analytic regularity for hyperfunction solutions to linear partial differential equations with analytic coefficients. In particular we show that some well known “sum of squares” operators, which satisfy Hörmander’s condition and consequently are hypoelliptic, admit hyperfunction solutions that are not smooth (in particular they are not distributions).     

Identification de la Non-Linearité D'Une équation Parabolique Quasilineaire

In this work, we study the problem of the determination of the non-linearity in a parabolic equation from measurements over its solution. This corresponds to an usual physical situation, in which the parameter depends on the state of the system (for instance, the heat conduction coefficient depends on the temperature). We first state the problem as a control problem, for which we show the existence of a solution, and we calculate the derivative of the criterion with respect to the nonlinearity without any assumption on the algebraic form of this latter. We finally give a numerical application of the algorithm, which shows that the situation which is here taken in account (estimation of a parameter function depending on the state) is much better, from the identification point of view, than the usually studied situation for distributed system (estimation of a parameter function depending on space and/or time variables).     

A singular integral equation with the Cauchy kernel on a closed interval in a class of distributions

In the present paper, we introduce the notion of a singular integral with the Cauchy kernel for distributions and consider a singular integral equation with the Cauchy kernel on a closed interval for the case in which the right-hand side is a distribution that admits a representation in the form of the sum of a distribution vanishing in neighborhoods of the endpoints and an ordinary function satisfying the Hölder condition. The solution is also sought in the form of a distribution. Distributions are treated as linear functionals on some test functions. We analyze the solvability of the equation in the class of distributions and obtain explicit formulas for the inversion of this equation, similar to formulas for ordinary solutions. To analyze the solvability of the singular integral equation, we use an approach based on the consideration of the Riemann boundary value problem for analytic functions with a generalized boundary condition. When stating and studying this problem, we use the results in [1, 2].Translated from Differentsialnye Uravneniya, Vol. 40, No. 9, 2004, pp. 1208–1218.Original Russian Text Copyright © 2004 by Setukha.     

Scenario tree generation and multi-asset financial optimization problems

We compare two popular scenario tree generation methods in the context of financial optimization: moment matching and scenario reduction. Using a simple problem with a known analytic solution, moment matching–when ensuring absence of arbitrage–replicates this solution precisely. On the other hand, even if the scenario trees generated by scenario reduction are arbitrage-free, the solutions are biased and highly variable. These results hold for correlated and uncorrelated asset returns, as well as for normal and non-normal returns.     

On the characterization of fisher information and stability of the least favorable lattice distributions

The paper is concerned with the stability properties of the least favorable distributions minimizing the Fisher information in a given class of distributions. The derivation of a least favorable distribution (the solution of a variational problem) is a necessary stage of the Huber minimax approach in robust estimation of a location parameter. Generally, the solutions of variational problems essentially depend on the regularity restrictions of a functional class. The stability of these optimal solutions to violations of the smoothness restrictions is studied under the lattice distribution classes. The discrete analogues of Fisher information are obtained in these cases. They have the form of the Hellinger metrics with the estimation of a real continuous location parameter and the form of the X² metrics with the estimation of an integer discrete location parameter. The analytical expressions for the corresponding least favorable discrete distributions are derived in some classes of lattice distributions by means of generating functions and Bellman's recursive functional equations of dynamic programming. These classes include the class of nondegenerate distributions with a restriction on the value of the density in the center of symmetry, the class of finite distributions, and the class of contaminated distributions. The obtained least favorable lattice distributions preserve the structure of their prototypes in the continuous case. These results show the stability of robust minimax solutions under different types of transitions from the continuous distribution to the discrete one. Proceedings of the Seminar on Stability Problems for Stochastic Models, Hajduszoboszló, Hungary, 1997, Part II.     

Use of 3D-transient analytical solution based on Green’s function to reduce computational time in inverse heat conduction problems

Inverse problems can be found in many areas of science and engineering and can be applied in different ways. Two examples can be cited: thermal properties estimation or heat flux function estimation in some engineering thermal process. Different techniques for the solution of inverse heat conduction problem (IHCP) can be found in literature. However, any inverse or optimization technique has a basic and common characteristic: the need to solve the direct problem solution several times. This characteristic is the cause of the great computational time consumed. In heat conduction problem, the time consumed is, usually, due to the use of numerical solutions of multidimensional models with refined mesh. In this case, if analytical solutions are available the computational time can be reduced drastically. This study presents the development and application of a 3D-transient analytical solution based on Green’s function. The inverse problem is due to the thermal properties estimation of conductors. The method is based on experimental determination of thermal conductivity and diffusivity using partially heated surface method without heat flux transducer. Originally developed to use numerical solution, this technique can, using analytical solution, estimate thermal properties faster and with better accuracy.     

A few logs suffice to build (almost) all trees (I)

A phylogenetic tree, also called an “evolutionary tree,” is a leaf‐labeled tree which represents the evolutionary history for a set of species, and the construction of such trees is a fundamental problem in biology. Here we address the issue of how many sequence sites are required in order to recover the tree with high probability when the sites evolve under standard Markov‐style i.i.d. mutation models. We provide analytic upper and lower bounds for the required sequence length, by developing a new polynomial time algorithm. In particular, we show when the mutation probabilities are bounded the required sequence length can grow surprisingly slowly (a power of log n) in the number n of sequences, for almost all trees. ©1999 John Wiley & Sons, Inc. Random Struct. Alg., 14, 153–184, 1999     

Greedy heuristics for the diameter-constrained minimum spanning tree problem

The diameter-constrained minimum spanning tree problem is an NP-hard combinatorial optimization problem that seeks a minimum cost spanning tree with a limit D imposed upon the length of any path in the tree. We begin by presenting four constructive greedy heuristics and, secondly, we present some second-order heuristics, performing some improvements on feasible solutions, hopefully leading to better objective function values. We present a heuristic with an edge exchange mechanism, another that transforms a feasible spanning tree solution into a feasible diameter-constrained spanning tree solution, and finally another with a repetitive mechanism. Computational results show that repetitive heuristics can improve considerably over the results of the greedy constructive heuristics, but using a huge amount of computation time. To obtain computational results, we use instances of the problem corresponding to complete graphs with a number of nodes between 20 and 60 and with the value of D varying between 4 and 9.     

Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem

The aim of this work is to introduce several proposals for combining two metaheuristics: variable neighborhood search (VNS) and estimation of distribution algorithms (EDAs). Although each of these metaheuristics has been previously hybridized in several ways, this paper constitutes the first attempt to combine both optimization methods. The different ways of combining VNS and EDAs will be classified into three groups. In the first group, we will consider combinations where the philosophy underlying VNS is embedded in EDAs. Considering different neighborhood spaces (points, populations or probability distributions), we will obtain instantiations for the approaches in this group. The second group of algorithms is obtained when probabilistic models (or any other machine learning paradigm) are used in order to exploit the good and bad shakes of the randomly generated solutions in a reduced variable neighborhood search. The last group of algorithms contains the results of alternating VNS and EDAs. An application of the first approach is presented in the protein side chain placement problem. The results obtained show the superiority of the hybrid algorithm in comparison with EDAs and VNS.     

Uncertainty in Phylogenetic Tree Estimates

Estimating phylogenetic trees is an important problem in evolutionary biology, environmental policy, and medicine. Although trees are estimated, their uncertainties are generally discarded in statistical models for tree-valued data. Here, we explicitly model the multivariate uncertainty of tree estimates. We consider both the cases where uncertainty information arises extrinsically (through covariate information) and intrinsically (through the tree estimates themselves). The latter case is applicable to any procedure for tree estimation, and thus has broad relevance to the entire field of phylogenetics. The importance of accounting for tree uncertainty in tree space is demonstrated in two case studies. In the first instance, differences between gene trees are small relative to their uncertainties, while in the second, the differences are relatively large. Our main goal is visualization of tree uncertainty, and we demonstrate advantages of our method with respect to reproducibility, speed, and preservation of topological differences compared to visualization based on multidimensional scaling. The proposal highlights that phylogenetic trees are estimated in an extremely high-dimensional space, resulting in uncertainty information that cannot be discarded. Most importantly, it is a method that allows biologists to diagnose whether differences between gene trees are biologically meaningful or due to uncertainty in estimation.