Alphabetic points in compositions and words

2021 ◽  
Vol 31 (4) ◽  
pp. 241-250
Author(s):  
Margaret Archibald ◽  
Aubrey Blecher ◽  
Arnold Knopfmacher
Keyword(s):  
Lower Bounds ◽  
Generating Functions ◽  
Upper And Lower Bounds ◽  
A Value

Abstract We use generating functions to account for alphabetic points (or the lack thereof) in compositions and words. An alphabetic point is a value j such that all the values to its left are not larger than j and all the values to its right are not smaller than j. We also provide the asymptotics for compositions and words which have no alphabetic points, as the size tends to infinity. This is achieved by the construction of upper and lower bounds which converge to each other, and in the latter case by probabilistic arguments.

scholarly journals A Note on the Symmetric Powers of the Standard Representation of $S_n$

10.37236/1484 ◽  
2000 ◽  
Vol 7 (1) ◽  
Author(s):  
D. Savitt ◽  
R. P. Stanley
Keyword(s):  
Lower Bounds ◽  
Generating Functions ◽  
Upper And Lower Bounds ◽  
Dimensional Representation ◽  
Standard Representation ◽  
Symmetric Powers

In this paper, we prove that the dimension of the space span- ned by the characters of the symmetric powers of the standard $n$-dimensional representation of $S_n$ is asymptotic to $n^2 / 2$. This is proved by using generating functions to obtain formulas for upper and lower bounds, both asymptotic to $n^2/2$, for this dimension. In particular, for $n \ge 7$, these characters do not span the full space of class functions on $S_n$.

On fractional linear bounds for probability generating functions

1986 ◽  
Vol 23 (4) ◽  
pp. 904-913 ◽  
Author(s):  
Claude Lefevre ◽  
Marc Hallin ◽  
Prakash Narayan
Keyword(s):  
Lower Bounds ◽  
Generating Functions ◽  
Probability Generating Function ◽  
Linear Functions ◽  
Upper And Lower Bounds ◽  
Finite Variance ◽  
Infinitely Divisible Distributions ◽  
Slight Improvement ◽  
Infinitely Divisible ◽  
The Family

The best upper and lower bounds for any probability generating function with mean m and finite variance are derived within the family of fractional linear functions with mean m. These are often intractable and simpler bounds, more useful for practical purposes, are then constructed. Direct applications in branching and epidemic theories are briefly presented; a slight improvement of the bounds is obtained for infinitely divisible distributions.

On fractional linear bounds for probability generating functions

1986 ◽  
Vol 23 (04) ◽  
pp. 904-913 ◽  
Author(s):  
Claude Lefevre ◽  
Marc Hallin ◽  
Prakash Narayan
Keyword(s):  
Lower Bounds ◽  
Generating Functions ◽  
Probability Generating Function ◽  
Linear Functions ◽  
Upper And Lower Bounds ◽  
Finite Variance ◽  
Infinitely Divisible Distributions ◽  
Slight Improvement ◽  
Infinitely Divisible ◽  
The Family

The best upper and lower bounds for any probability generating function with mean m and finite variance are derived within the family of fractional linear functions with mean m. These are often intractable and simpler bounds, more useful for practical purposes, are then constructed. Direct applications in branching and epidemic theories are briefly presented; a slight improvement of the bounds is obtained for infinitely divisible distributions.

Improving intervals

1993 ◽  
Vol 3 (2) ◽  
pp. 153-169
Author(s):  
W. Ken Jackson ◽  
F. Warren Burton
Keyword(s):  
Lower Bounds ◽  
Branch And Bound ◽  
Data Type ◽  
Parallel Program ◽  
Upper And Lower Bounds ◽  
A Value ◽  
Parallel Branch And Bound ◽  
Simple Implementation ◽  
Alpha Beta

AbstractWe show how improving values (Burton, 1991) can be extended to handle both upper and lower bounds. The result is a new data type, called improving intervals. We give a simple implementation of improving intervals that uses a list of successively tighter bounds to represent a value. A program using improving intervals can be evaluated as a parallel program using speculative evaluation. The utility of improving intervals is demonstrated through two programs: parallel alpha-beta and parallel branch-and-bound

On fractional linear bounds for probability generating functions

1986 ◽  
Vol 23 (04) ◽  
pp. 904-913
Author(s):  
Claude Lefevre ◽  
Marc Hallin ◽  
Prakash Narayan
Keyword(s):  
Lower Bounds ◽  
Generating Functions ◽  
Probability Generating Function ◽  
Linear Functions ◽  
Upper And Lower Bounds ◽  
Finite Variance ◽  
Infinitely Divisible Distributions ◽  
Slight Improvement ◽  
Infinitely Divisible ◽  
The Family

The best upper and lower bounds for any probability generating function with meanmand finite variance are derived within the family of fractional linear functions with meanm. These are often intractable and simpler bounds, more useful for practical purposes, are then constructed. Direct applications in branching and epidemic theories are briefly presented; a slight improvement of the bounds is obtained for infinitely divisible distributions.

scholarly journals On the Generalized Distance Energy of Graphs

Mathematics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 17 ◽  
Author(s):  
Abdollah Alhevaz ◽  
Maryam Baghipur ◽  
Hilal A. Ganie ◽  
Yilun Shang
Keyword(s):  
Lower Bounds ◽  
Complete Graph ◽  
Connected Graph ◽  
Distance Matrix ◽  
Wiener Index ◽  
Diagonal Matrix ◽  
Upper And Lower Bounds ◽  
Star Graph ◽  
Extremal Graphs ◽  
Generalized Distance

The generalized distance matrix D α ( G ) of a connected graph G is defined as D α ( G ) = α T r ( G ) + ( 1 − α ) D ( G ) , where 0 ≤ α ≤ 1 , D ( G ) is the distance matrix and T r ( G ) is the diagonal matrix of the node transmissions. In this paper, we extend the concept of energy to the generalized distance matrix and define the generalized distance energy E D α ( G ) . Some new upper and lower bounds for the generalized distance energy E D α ( G ) of G are established based on parameters including the Wiener index W ( G ) and the transmission degrees. Extremal graphs attaining these bounds are identified. It is found that the complete graph has the minimum generalized distance energy among all connected graphs, while the minimum is attained by the star graph among trees of order n.

scholarly journals Hadamard k-fractional inequalities of Fejér type for GA-s-convex mappings and applications

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Hui Lei ◽  
Gou Hu ◽  
Zhi-Jie Cao ◽  
Ting-Song Du
Keyword(s):  
Lower Bounds ◽  
Hypergeometric Functions ◽  
Upper And Lower Bounds ◽  
Weighted Inequalities ◽  
Convex Mappings ◽  
Special Means

Abstract The main aim of this paper is to establish some Fejér-type inequalities involving hypergeometric functions in terms of GA-s-convexity. For this purpose, we construct a Hadamard k-fractional identity related to geometrically symmetric mappings. Moreover, we give the upper and lower bounds for the weighted inequalities via products of two different mappings. Some applications of the presented results to special means are also provided.

scholarly journals On the Second-Largest Reciprocal Distance Signless Laplacian Eigenvalue

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 512
Author(s):  
Maryam Baghipur ◽  
Modjtaba Ghorbani ◽  
Hilal A. Ganie ◽  
Yilun Shang
Keyword(s):  
Lower Bounds ◽  
Complete Graph ◽  
Distance Matrix ◽  
Upper And Lower Bounds ◽  
Laplacian Eigenvalue ◽  
Signless Laplacian ◽  
Graph Parameters ◽  
Simple Connected Graph ◽  
Second Largest Eigenvalue ◽  
Reciprocal Distance Matrix

The signless Laplacian reciprocal distance matrix for a simple connected graph G is defined as RQ(G)=diag(RH(G))+RD(G). Here, RD(G) is the Harary matrix (also called reciprocal distance matrix) while diag(RH(G)) represents the diagonal matrix of the total reciprocal distance vertices. In the present work, some upper and lower bounds for the second-largest eigenvalue of the signless Laplacian reciprocal distance matrix of graphs in terms of various graph parameters are investigated. Besides, all graphs attaining these new bounds are characterized. Additionally, it is inferred that among all connected graphs with n vertices, the complete graph Kn and the graph Kn−e obtained from Kn by deleting an edge e have the maximum second-largest signless Laplacian reciprocal distance eigenvalue.

scholarly journals Multilevel Monte Carlo methods and lower–upper bounds in initial margin computations

2020 ◽  
Vol 26 (2) ◽  
pp. 131-161
Author(s):  
Florian Bourgey ◽  
Stefano De Marco ◽  
Emmanuel Gobet ◽  
Alexandre Zhou
Keyword(s):  
Monte Carlo ◽  
Lower Bounds ◽  
Asymptotic Optimality ◽  
Upper Bounds ◽  
Upper And Lower Bounds ◽  
Independent Random Variables ◽  
Outer Function ◽  
Control Problems ◽  
Multilevel Monte Carlo ◽  
Primal Dual

AbstractThe multilevel Monte Carlo (MLMC) method developed by M. B. Giles [Multilevel Monte Carlo path simulation, Oper. Res. 56 2008, 3, 607–617] has a natural application to the evaluation of nested expectations {\mathbb{E}[g(\mathbb{E}[f(X,Y)|X])]}, where {f,g} are functions and {(X,Y)} a couple of independent random variables. Apart from the pricing of American-type derivatives, such computations arise in a large variety of risk valuations (VaR or CVaR of a portfolio, CVA), and in the assessment of margin costs for centrally cleared portfolios. In this work, we focus on the computation of initial margin. We analyze the properties of corresponding MLMC estimators, for which we provide results of asymptotic optimality; at the technical level, we have to deal with limited regularity of the outer function g (which might fail to be everywhere differentiable). Parallel to this, we investigate upper and lower bounds for nested expectations as above, in the spirit of primal-dual algorithms for stochastic control problems.

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