A characterization of the multivariate excess wealth ordering

In this paper, some new properties of the upper-corrected orthant of a random vector are proved. The univariate right-spread or excess wealth function, introduced by Fernández-Ponce et al. (1996), is extended to multivariate random vectors, and some properties of this multivariate function are studied. Later, this function was used to define the excess wealth ordering by Shaked and Shanthikumar (1998) and Fernández-Ponce et al. (1998). The multivariate excess wealth function enable us to define a new stochastic comparison which is weaker than the multivariate dispersion orderings. Also, some properties relating the multivariate excess wealth order with stochastic dependence are described.     

An Interactive Multiple Criteria Approach to Ranking Alternatives

In this paper we are concerned with ranking various orderings of a set of alternatives to a composite order as a multiple criteria problem. The orderings (called preference orderings) can be real preference orderings or any natural orderings. The objective is to find the most preferred order of the decision maker using the preference orderings as criteria.In principle, the problem can be formulated as a multiple objective linear programming problem using the model of Bowman and Colantoni and then solved with the interactive method proposed by Zionts and Wallenius. However, the fact that we are dealing with integer variables prohibits us from applying this approach as such. We discuss the problem formulation and propose a modified approach to that of Zionts and Wallenius for solving the problem.     

Monotone norms and tensor products

Various refinements of orthant monotonicity for norms are studied. A partial order is induced in the tensor product of two partially ordered vector spaces. The induced norm in the tensor product is shown to be orthant monotone in certain cases.     

Monotone norms and tensor products

Various refinements of orthant monotonicity for norms are studied. A partial order is induced in the tensor product of two partially ordered vector spaces. The induced norm in the tensor product is shown to be orthant monotone in certain cases.     

Stability verification for monotone systems using homotopy algorithms

A monotone self-mapping of the nonnegative orthant induces a monotone discrete-time dynamical system which evolves on the same orthant. If with respect to this system the origin is attractive then there must exist points whose image under the monotone map is strictly smaller than the original point, in the component-wise partial ordering. Here it is shown how such points can be found numerically, leading to a recipe to compute order intervals that are contained in the region of attraction and where the monotone map acts essentially as a contraction. An important application is the numerical verification of so-called generalized small-gain conditions that appear in the stability theory of large-scale systems.     

Stochastic orderings, folded beta distributions and fairness in coin flips

This paper studies stochastic orderings for folded beta distributions. A necessary and sufficient condition for one folded beta random variable to be larger than another in likelihood ratio order is obtained. The work is motivated by the recent results of Porzio and Ragozini on stochastic orderings for folded binomial distributions. Beta distributions are commonly used to model proportions or probabilities, particularly as conjugate distributions for binomial random variables. Stochastic orderings may arise, for instance, in considerations of fairness in coin flips.     

Approximate factorizations with modified S/P consistently ordered M-factors

Preconditioned iterative methods are widely used to solve linear systems such as those arising from the finite element formulation of boundary value problems and approximate factorizations are widely used as preconditioners. The ordering of the unknowns is therefore an important issue because it has a strong influence on the convergence behaviour of the iteration method while it is also a decisive aspect for their parallel implementation. Consistent orderings are attractive for parallel implementations and it has been shown that some subclasses of these orderings also enhance the convergence behaviour of the associated iteration methods. This has in particular been shown for the so-called S/P consistent orderings. A wider definition of this class of orderings has recently been proposed and we investigate here how approximate factorizations should be implemented when using such more general orderings (still called S/P consistent) in order to keep their expected high convergence properties. A simple practical conclusion is suggested, supported by both theoretical and numerical arguments.     

Moment Approximations for Set-Semidefinite Polynomials

The set of polynomials that are nonnegative over a subset of the nonnegative orthant (we call them set-semidefinite) have many uses in optimization. A common example of this type set is the set of copositive matrices, where we are effectively considering nonnegativity over the entire nonnegative orthant and are restricted to homogeneous polynomials of degree two. Lasserre (SIAM J. Optim., 21(3):864–885, 2011) has previously considered a method using moments in order to provide an outer approximation to this set, for nonnegativity over a general subset of the real space. In this paper, we shall show that, in the special case of considering nonnegativity over a subset of the nonnegative orthant, we can provide a new outer approximation hierarchy. This is based on restricting moment matrices to be completely positive, and it is at least as good as Lasserre’s method. This can then be relaxed to give tractable approximations that are still at least as good as Lasserre’s method. In doing this, we also provide interesting new insights into the use of moments in constructing these approximations.     

Majorization and arrangement orderings in open queueing networks

Consider an open Jackson network of queues. Majorization and arrangement orderings are studied to order, respectively, various loading and server-assignment policies. It is shown that under these order relations, stochastic and likelihood ratio orderings can be established for the maximum and the minimum queue lengths and for the total number of jobs in the network. Stochastic majorization and stochastic orderings are also established, respectively, for the queue-length vector and the associated order-statistic vector. Implications of the results on loading and assignment decisions are discussed.     

Some new classes of stochastic order relations among arithmetic random variables, with applications in actuarial sciences

Recently, Fishbum and Lavalle (1995) and Lefèvre and Utev (1996) have considered some stochastic order relations specific for arithmetic random variables. The present work is concerned with these orderings, together with two other classes of stochastic order relations closely related. First, attention is paid to characterizations and various properties of all these orderings. Then, sufficient conditions of crossing-type for the two new classes of orderings are derived and extrema among discrete random variables are deduced. This is applied in actuarial sciences to obtain new bounds for the classical single life premiums as well as for the probability of ruin in the compound binomial risk model.     

Computing the Stationary Distribution of an SRBM in an Orthant with Applications to Queueing Networks

In [15], a BNAfm (Brownian network analyzer with finite element method) algorithm was developed for computing the stationary distribution of a semimartingale reflecting Brownian motion (SRBM) in a hypercube. In this companion paper, that BNAfm algorithm is extended to computing the stationary distribution of an SRBM in an orthant, which is achieved by constructing a converging sequence of SRBMs in hypercubes. The SRBM in the orthant serves as an approximation model of queueing networks with infinite buffers. We show that the constructed sequence of SRBMs in the hypercubes converges weakly to the SRBM in the orthant as the hypercubes approach the orthant. Under the conjecture that the set of the stationary distributions of the SRBMs in the hypercubes is relatively compact, we prove that the sequence of the stationary distributions of the SRBMs in the hypercubes converges weakly to the stationary distribution of the SRBM in the orthant. A three-machine job shop example is presented to illustrate the effectiveness of the SRBM approximation model and our BNAfm algorithm. The BNAfm algorithm is shown to produce good estimates for stationary probabilities of queueing networks.     

Representable Lexicographic Products

A linear ordering is said to be representable if it can be order-embedded into the reals. Representable linear orderings have been characterized as those which are separable in the order topology and have at most countably many jumps. We use this characterization to study the representability of a lexicographic product of linear orderings. First we count the jumps in a lexicographic product in terms of the number of jumps in its factors. Then we relate the separability of a lexicographic product to properties of its factors, and derive a classification of representable lexicographic products. This revised version was published online in September 2006 with corrections to the Cover Date.     

Some characterizations of orthant monotonic norms

In real n-space the orthant monotonic norms of Gries [5] can be given a new characterization similar to one for monotonic norms: a norm is orthant monotonic if and only if for every D=diag(δ₁,δ₂,…,δ_n)?0, the operator norm of D equals max δ_i. This gives an alternative proof to Gries's: a norm is orthant monotonic if and only if its dual norm is orthant monotonic. Also, it follows that the principal axis vectors are self-dual for orthant monotonic norms.     

Orthant tail dependence of multivariate extreme value distributions

The orthant tail dependence describes the relative deviation of upper- (or lower-) orthant tail probabilities of a random vector from similar orthant tail probabilities of a subset of its components, and can be used in the study of dependence among extreme values. Using the conditional approach, this paper examines the extremal dependence properties of multivariate extreme value distributions and their scale mixtures, and derives the explicit expressions of orthant tail dependence parameters for these distributions. Properties of the tail dependence parameters, including their relations with other extremal dependence measures used in the literature, are discussed. Various examples involving multivariate exponential, multivariate logistic distributions and copulas of Archimedean type are presented to illustrate the results.     

Linear Complementarity Problems on Extended Second Order Cones

In this paper, we study the linear complementarity problems on extended second order cones. We convert a linear complementarity problem on an extended second order cone into a mixed complementarity problem on the non-negative orthant. We state necessary and sufficient conditions for a point to be a solution of the converted problem. We also present solution strategies for this problem, such as the Newton method and Levenberg–Marquardt algorithm. Finally, we present some numerical examples.     

Regular and singular orthants of tridiagonal matrices

With respect to a tridiagonal matrix with variable diagonal vector g, an orthant is said to be regular (singular) if the matrix is nonsingular (singular) for all g in it. We give necessary and sufficient conditions for an orthant to be regular or singular. Our idea is based on observations of a simple two-by-two matrix, and all the results obtained are original and self-contained.     

Fuzzy preference orderings in group decision making

In this paper, some use of fuzzy preference orderings in group decision making is discussed. First, fuzzy preference orderings are defined as fuzzy binary relations satisfying reciprocity and max-min transitivity. Then, particularly in the case where individual preferences are represented by utility functions (utility values), group fuzzy preference orderings of which fuzziness is caused by differences or diversity of individual opinions are defined. Those orderings might be useful for proceeding the group decision making process smoothly, in the same manner as the extended contributive rule method.     

Row-ordering schemes for sparse givens transformations. II. implicit graph model

A new graph model is presented to study the row annihilation and row ordering problems in the QR decomposition of sparse matrices using Givens rotations. The graph-theoretic results obtained can be used to derive good row orderings for certain column orderings, such as width-1 and width-2 nested dissection orderings. This model is different from the bipartite-graph model introduced in [6]. We refer to the new model as implicit because the rows are not represented explicitly by nodes, in contrast to the bipartite-graph model, where the rows are represented by nodes in a bipartite graph.     

An application of homotopy to solving linear programs

Consider a linear program inm inequality constraints andn nonnegative variables. An application of homotopy to the problem gives an algorithm similar to Dantzig's self-dual method. Howeve, the homotopy approach allows one to recognize several previously undescribed and potentially interesting properties. For example, the algorithm can be initiated in such a way as to produce a path which is primal-dual feasible. Moreover, one can theoretically identify an orthant with the property that if one initiates the algorithm at any point in that orthant then, after a ‘phase I’ requiring at most min{m, n} pivots, convergence is obtained in one step.     

Variable orderings and the size of OBDDs for random partially symmetric Boolean functions

The size of ordered binary decision diagrams (OBDDs) strongly depends on the chosen variable ordering. It is an obvious heuristic to use symmetric variable orderings, i.e., variable orderings where symmetric variables are arranged adjacently. In order to evaluate this heuristic, methods for estimating the OBDD size for random partially symmetric functions are presented. Characterizations of cases where, with high probability, only symmetric variable orderings and, with high probability, only nonsymmetric variable orderings lead to minimum OBDD size are obtained. For this analysis estimates for the number of different blocks of random Boolean matrices are used. © 1998 John Wiley & Sons, Inc. Random Struct. Alg., 13, 49–70, 1998