Qualitative possibilistic influence diagrams based on qualitative possibilistic utilities

This paper proposes a new approach for decision making under uncertainty based on influence diagrams and possibility theory. The so-called qualitative possibilistic influence diagrams extend standard influence diagrams in order to avoid difficulties attached to the specification of both probability distributions relative to chance nodes and utilities relative to value nodes. In fact, generally, it is easier for experts to quantify dependencies between chance nodes qualitatively via possibility distributions and to provide a preferential relation between different consequences. In such a case, the possibility theory offers a suitable modeling framework. Different combinations of the quantification between chance and utility nodes offer several kinds of possibilistic influence diagrams. This paper focuses on qualitative ones and proposes an indirect evaluation method based on their transformation into possibilistic networks. The proposed approach is implemented via a possibilistic influence diagram toolbox (PIDT).     

Sequential valuation networks for asymmetric decision problems

This paper deals with representation and solution of asymmetric decision problems. We describe a new representation called sequential valuation networks that is a hybrid of Covaliu and Oliver’s sequential decision diagrams and Shenoy’s valuation networks. The solution algorithm is based on the idea of decomposing a large asymmetric problem into smaller sub-problems and then using the fusion algorithm of valuation networks to solve the sub-problems. Sequential valuation networks inherit many of the strengths of sequential decision diagrams and valuation networks while overcoming many of their shortcomings. We illustrate our technique by representing and solving a modified version of Covaliu and Oliver’s [Manage. Sci. 41(12) (1995) 1860] Reactor problem in complete detail.     

Probabilistic decision graphs for optimization under uncertainty

This paper provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty. We give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length of the decision sequence, we present a couple of approaches for calculating approximate solutions. The modeling scope of the influence diagram is limited to so-called symmetric decision problems. This limitation has motivated the development of alternative representation languages, which enlarge the class of decision problems that can be modeled efficiently. We present some of these alternative frameworks and demonstrate their expressibility using several examples. Finally, we provide a list of software systems that implement the frameworks described in the paper.     

Probabilistic decision graphs for optimization under uncertainty

This paper provides a survey on probabilistic decision graphs for modeling and solving decision problems under uncertainty. We give an introduction to influence diagrams, which is a popular framework for representing and solving sequential decision problems with a single decision maker. As the methods for solving influence diagrams can scale rather badly in the length of the decision sequence, we present a couple of approaches for calculating approximate solutions. The modeling scope of the influence diagram is limited to so-called symmetric decision problems. This limitation has motivated the development of alternative representation languages, which enlarge the class of decision problems that can be modeled efficiently. We present some of these alternative frameworks and demonstrate their expressibility using several examples. Finally, we provide a list of software systems that implement the frameworks described in the paper.     

Decision making with hybrid influence diagrams using mixtures of truncated exponentials

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials can be used to approximate utility functions. This paper introduces MTE influence diagrams, which can represent decision problems without restrictions on the relationships between continuous and discrete chance variables, without limitations on the distributions of continuous chance variables, and without limitations on the nature of the utility functions. In MTE influence diagrams, all probability distributions and the joint utility function (or its multiplicative factors) are represented by MTE potentials and decision nodes are assumed to have discrete state spaces. MTE influence diagrams are solved by variable elimination using a fusion algorithm.     

An exact algorithm for graph partitioning

An exact algorithm is presented for solving edge weighted graph partitioning problems. The algorithm is based on a branch and bound method applied to a continuous quadratic programming formulation of the problem. Lower bounds are obtained by decomposing the objective function into convex and concave parts and replacing the concave part by an affine underestimate. It is shown that the best affine underestimate can be expressed in terms of the center and the radius of the smallest sphere containing the feasible set. The concave term is obtained either by a constant diagonal shift associated with the smallest eigenvalue of the objective function Hessian, or by a diagonal shift obtained by solving a semidefinite programming problem. Numerical results show that the proposed algorithm is competitive with state-of-the-art graph partitioning codes.     

Influence diagrams with super value nodes involving imprecise information

The concept of super value nodes was established to allow dynamic programming to be performed within the theory of influence diagrams and to reduce the computational complexity in solving problems by means of influence diagrams. This paper is focused on how influence diagrams with super value nodes are affected by the presence of imprecise information. We analyze how to reduce the complexity when evaluating an influence diagram in this framework by modelling these kinds of nodes and random magnitudes in terms of fuzzy random variables. Finally, an applied example of the theoretical results is developed.     

A comparison of two approaches for solving unconstrained influence diagrams

Influence diagrams and decision trees represent the two most common frameworks for specifying and solving decision problems. As modeling languages, both of these frameworks require that the decision analyst specifies all possible sequences of observations and decisions (in influence diagrams, this requirement corresponds to the constraint that the decisions should be temporarily linearly ordered). Recently, the unconstrained influence diagram was proposed to address this drawback. In this framework, we may have a partial ordering of the decisions, and a solution to the decision problem therefore consists not only of a decision policy for the various decisions, but also of a conditional specification of what to do next. Relative to the complexity of solving an influence diagram, finding a solution to an unconstrained influence diagram may be computationally very demanding w.r.t. both time and space. Hence, there is a need for efficient algorithms that can deal with (and take advantage of) the idiosyncrasies of the language. In this paper we propose two such solution algorithms. One resembles the variable elimination technique from influence diagrams, whereas the other is based on conditioning and supports any-space inference. Finally, we present an empirical comparison of the proposed methods.     

Comparisons between multiplicative and additive Schwarz iterations in domain decomposition methods

We consider the multiplicative and additive Schwarz methods for solving linear systems of equations and we compare their asymptotic rate of convergence. Moreover, we compare the multiplicative Schwarz method with the weighted restricted additive Schwarz method. We prove that the multiplicative Schwarz method is the fastest method among these three. Our comparisons can be done in the case of exact and inexact subspaces solves. In addition, we analyse two ways of adding a coarse grid correction – multiplicatively or additively. Mathematics Subject Classification (1991):65F10, 65F35, 65M55     

Solving a kind of boundary-value problem for ordinary differential equations using Fermi—The next generation CUDA computing architecture

The aim of this paper is to show that a special kind of boundary value problem for solving second-order ordinary differential equations can be efficiently solved on modern heterogeneous computer architectures based on CPU and GPU Fermi processors. Such a problem reduces to the problem of solving a large tridiagonal system of linear equations with an almost Toeplitz structure. The considered algorithm is based on the recently developed divide and conquer method for solving linear recurrence systems with constant coefficients.     

Nonanticipative duality,relaxations, and formulations for chance-constrained stochastic programs

We propose two new Lagrangian dual problems for chance-constrained stochastic programs based on relaxing nonanticipativity constraints. We compare the strength of the proposed dual bounds and demonstrate that they are superior to the bound obtained from the continuous relaxation of a standard mixed-integer programming (MIP) formulation. For a given dual solution, the associated Lagrangian relaxation bounds can be calculated by solving a set of single scenario subproblems and then solving a single knapsack problem. We also derive two new primal MIP formulations and demonstrate that for chance-constrained linear programs, the continuous relaxations of these formulations yield bounds equal to the proposed dual bounds. We propose a new heuristic method and two new exact algorithms based on these duals and formulations. The first exact algorithm applies to chance-constrained binary programs, and uses either of the proposed dual bounds in concert with cuts that eliminate solutions found by the subproblems. The second exact method is a branch-and-cut algorithm for solving either of the primal formulations. Our computational results indicate that the proposed dual bounds and heuristic solutions can be obtained efficiently, and the gaps between the best dual bounds and the heuristic solutions are small.     

Solving a kind of boundary-value problem for ordinary differential equations using Fermi—The next generation CUDA computing architecture

The aim of this paper is to show that a special kind of boundary value problem for solving second-order ordinary differential equations can be efficiently solved on modern heterogeneous computer architectures based on CPU and GPU Fermi processors. Such a problem reduces to the problem of solving a large tridiagonal system of linear equations with an almost Toeplitz structure. The considered algorithm is based on the recently developed divide and conquer method for solving linear recurrence systems with constant coefficients.     

Variable elimination for influence diagrams with super value nodes

In the original formulation of influence diagrams (IDs), each model contained exactly one utility node. In 1990, Tatman and Shachtar introduced the possibility of having super value nodes that represent a combination of their parents’ utility functions. They also proposed an arc-reversal algorithm for IDs with super value nodes. In this paper we propose a variable-elimination algorithm for influence diagrams with super value nodes which is faster in most cases, requires less memory in general, introduces much fewer redundant (i.e., unnecessary) variables in the resulting policies, may simplify sensitivity analysis, and can speed up inference in IDs containing canonical models, such as the noisy OR.     

A linear-time algorithm for solving the molecular distance geometry problem with exact inter-atomic distances

We describe a linear-time algorithm for solving the molecular distance geometry problem with exact distances between all pairs of atoms. This problem needs to be solved in every iteration of general distance geometry algorithms for protein modeling such as the EMBED algorithm by Crippen and Havel (Distance Geometry and Molecular Conformation, Wiley, 1988). However, previous approaches to the problem rely on decomposing an distance matrix or minimizing an error function and require O(n²) to O(³) floating point operations. The linear-time algorithm will provide a much more efficient approach to the problem, especially in large-scale applications. It exploits the problem structure and hence is able to identify infeasible data more easily as well.     

Inexact Newton methods for the nonlinear complementarity problem

An exact Newton method for solving a nonlinear complementarity problem consists of solving a sequence of linear complementarity subproblems. For problems of large size, solving the subproblems exactly can be very expensive. In this paper we study inexact Newton methods for solving the nonlinear, complementarity problem. In such an inexact method, the subproblems are solved only up to a certain degree of accuracy. The necessary accuracies that are needed to preserve the nice features of the exact Newton method are established and analyzed. We also discuss some extensions as well as an application. This research was based on work supported by the National Science Foundation under grant ECS-8407240.     

群体多指标决策的偏好集结方法

研究多个指标条件下,利用个体决策结果形成群体一致偏好的方法、假设个体有加性效用函数,将个体多指标效用函数表示成单个指标评价函数的加权和,群体指标评价函数表示成个体指标评价函数的加权和.通过协商指标权重、指标评价函数、支付意愿三个参数,成对个体达成双方一致.提出了(n-1)对个体之间达成双方一致,从而得出群体效用函数的决策方法,这种分析框架同样可以扩展到联盟协商一致中.     

Archimedeaness and additive utility on totally ordered semigroups

We study problems concerning the existence of additive utility funtions defined on totally ordered semigroups. The existence of an additive utility function on a semigroup is characterized by means of conditions that are similar, but not equivalent, to Archimedeaness. This fact is used to analyze the existence of utility representations (not necessarily additive) on totally ordered Abelian groups. In this direction, we show that the positive cone of a representable totally ordered Abelian group admits a countable partition into Archimedean semigroups. All the semigroups in that partition are representable by means of a utility function, but at most one is additively representable. Communicated by M. W. Mislove     

Smoothed penalty algorithms for optimization of nonlinear models

We introduce an algorithm for solving nonlinear optimization problems with general equality and box constraints. The proposed algorithm is based on smoothing of the exact l ₁-penalty function and solving the resulting problem by any box-constraint optimization method. We introduce a general algorithm and present theoretical results for updating the penalty and smoothing parameter. We apply the algorithm to optimization problems for nonlinear traffic network models and report on numerical results for a variety of network problems and different solvers for the subproblems.     

Solving some vector subset problems by Voronoi diagrams

We propose a general approach to solving some vector subset problems in a Euclidean space that is based on higher-order Voronoi diagrams. In the case of a fixed space dimension, this approach allows us to find optimal solutions to these problems in polynomial time which is better than the runtime of available algorithms.