scholarly journals Linear programming upper bounds on permutation code sizes from coherent configurations related to the Kendall-tau distance metric

2012 ◽  
Author(s):  
Fabian Lim ◽  
Manabu Hagiwara
Keyword(s):  
Linear Programming ◽  
Upper Bounds ◽  
Distance Metric ◽  
Permutation Code ◽  
Kendall Tau Distance ◽  
Coherent Configurations

scholarly journals New Upper Bounds for the Size of Permutation Codes via Linear Programming

10.37236/407 ◽  
2010 ◽  
Vol 17 (1) ◽  
Author(s):  
Mathieu Bogaerts
Keyword(s):  
Linear Programming ◽  
Permutation Group ◽  
Association Scheme ◽  
Optimization Problem ◽  
Hamming Distance ◽  
Upper Bounds ◽  
Linear Inequalities ◽  
Permutation Codes ◽  
Permutation Code ◽  
Irreducible Characters

An $(n,d)$-permutation code of size $s$ is a subset $C$ of $S_n$ with $s$ elements such that the Hamming distance $d_H$ between any two distinct elements of $C$ is at least equal to $d$. In this paper, we give new upper bounds for the maximal size $\mu(n,d)$ of an $(n,d)$-permutation code of degree $n$ with $11\le n\le 14$. In order to obtain these bounds, we use the structure of association scheme of the permutation group $S_n$ and the irreducible characters of $S_n$. The upper bounds for $\mu(n,d)$ are determined solving an optimization problem with linear inequalities.

scholarly journals Verifying a Solver for Linear Mixed Integer Arithmetic in Isabelle/HOL

2020 ◽  
pp. 233-250
Author(s):  
Ralph Bottesch ◽  
Max W. Haslbeck ◽  
Alban Reynaud ◽  
René Thiemann
Keyword(s):  
Linear Programming ◽  
Branch And Bound ◽  
Decision Procedure ◽  
Simplex Algorithm ◽  
Upper Bounds ◽  
Mixed Integer ◽  
Small Solution ◽  
Code Generator ◽  
Integer Arithmetic ◽  
Termination Proofs

AbstractWe implement a decision procedure for linear mixed integer arithmetic and formally verify its soundness in Isabelle/HOL. We further integrate this procedure into one application, namely into , a formally verified certifier to check untrusted termination proofs. This checking involves assertions of unsatisfiability of linear integer inequalities; previously, only a sufficient criterion for such checks was supported. To verify the soundness of the decision procedure, we first formalize the proof that every satisfiable set of linear integer inequalities also has a small solution, and give explicit upper bounds. To this end we mechanize several important theorems on linear programming, including statements on integrality and bounds. The procedure itself is then implemented as a branch-and-bound algorithm, and is available in several languages via Isabelle’s code generator. It internally relies upon an adapted version of an existing verified incremental simplex algorithm.

Chandpur Enterprises Limited, Steel Division

2017 ◽  
pp. 1-4
Author(s):  
Samuel E. Bodily ◽  
Akshay Mittal
Keyword(s):  
Linear Programming ◽  
Linear Model ◽  
Raw Materials ◽  
Nonlinear Models ◽  
Upper Bounds ◽  
Raw Material ◽  
Shadow Prices ◽  
Lower And Upper Bounds ◽  
Optimization Function ◽  
Introductory Class

The managing director of a steel plant faces the decision of how much of each raw material to order for the plant for the following month. Due to lower and upper bounds on the amounts of each raw material in a batch and varying amounts of electricity and time consumed for different raw materials, one can't simply use the cheapest raw material. A linear program and the solver optimization function of Excel will provide the optimal amounts that meet the constraints. Interestingly, the best mixture for a batch is not the best mixture for a monthly plan. Shadow prices indicate the value of relaxing constraints. The typical monthly model from a student will be nonlinear, although it can be written as a linear model. This case provides the basis for an introductory class on linear programming and linear versus nonlinear models.

scholarly journals A dual-inexact fuzzy stochastic model for water resources management and non-point source pollution mitigation under multiple uncertainties

2014 ◽  
Vol 18 (5) ◽  
pp. 1793-1803 ◽  
Author(s):  
C. Dong ◽  
Q. Tan ◽  
G.-H. Huang ◽  
Y.-P. Cai
Keyword(s):  
Linear Programming ◽  
Point Source ◽  
Upper Bounds ◽  
Fuzzy Linear Programming ◽  
Chance Constrained Programming ◽  
Lower And Upper Bounds ◽  
Point Source Pollution ◽  
Pollution Mitigation ◽  
Trade Offs ◽  
Non Point Source Pollution

Abstract. In this research, a dual-inexact fuzzy stochastic programming (DIFSP) method was developed for supporting the planning of water and farmland use management system considering the non-point source pollution mitigation under uncertainty. The random boundary interval (RBI) was incorporated into DIFSP through integrating fuzzy linear programming (FLP) and chance-constrained programming (CCP) approaches within an interval linear programming (ILP) framework. This developed method could effectively tackle the uncertainties expressed as intervals and fuzzy sets. Moreover, the lower and upper bounds of RBI are continuous random variables, and the correlation existing between the lower and upper bounds can be tackled in RBI through the joint probability distribution function. And thus the subjectivity of decision making is greatly reduced, enhancing the stability and robustness of obtained solutions. The proposed method was then applied to solve a water and farmland use planning model (WFUPM) with non-point source pollution mitigation. The generated results could provide decision makers with detailed water supply–demand schemes involving diversified water-related activities under preferred satisfaction degrees. These useful solutions could allow more in-depth analyses of the trade-offs between humans and environment, as well as those between system optimality and reliability. In addition, comparative analyses on the solutions obtained from ICCP (Interval chance-constraints programming) and DIFSP demonstrated the higher application of this developed approach for supporting the water and farmland use system planning.

scholarly journals Upper bounds for packings of spheres of several radii

2014 ◽  
Vol 2 ◽  
Author(s):  
DAVID DE LAAT ◽  
FERNANDO MÁRIO DE OLIVEIRA FILHO ◽  
FRANK VALLENTIN
Keyword(s):  
Linear Programming ◽  
Euclidean Space ◽  
Semidefinite Programming ◽  
Unit Sphere ◽  
Upper Bound ◽  
Convex Bodies ◽  
Classical Problem ◽  
Upper Bounds ◽  
Sphere Packings ◽  
Spherical Caps

AbstractWe give theorems that can be used to upper bound the densities of packings of different spherical caps in the unit sphere and of translates of different convex bodies in Euclidean space. These theorems extend the linear programming bounds for packings of spherical caps and of convex bodies through the use of semidefinite programming. We perform explicit computations, obtaining new bounds for packings of spherical caps of two different sizes and for binary sphere packings. We also slightly improve the bounds for the classical problem of packing identical spheres.

BOUNDED LINEAR PROGRAMS WITH TRAPEZOIDAL FUZZY NUMBERS

2010 ◽  
Vol 18 (03) ◽  
pp. 269-286 ◽  
Author(s):  
ALI EBRAHIMNEJAD ◽  
SEYED HADI NASSERI ◽  
FARHAD HOSSEINZADEH LOTFI
Keyword(s):  
Linear Programming ◽  
Fuzzy Numbers ◽  
Natural Extension ◽  
Upper Bounds ◽  
Linear Programs ◽  
Bounded Linear ◽  
New Approach ◽  
Trapezoidal Fuzzy Numbers ◽  
Linear Programming Problems ◽  
Crisp Linear Programming

Recently Ganesan and Veeramani introduced a new approach for solving a kind of linear programming problems involving symmetric trapezoidal fuzzy numbers without converting them to the crisp linear programming problems. But their approach is not efficient for situations in which some or all variables are restricted to lie within fuzzy lower and fuzzy upper bounds. In this paper, by a natural extension of their approach we obtain some new results leading to a new method to overcome this shortcoming.

Bounded Primal Simplex Algorithm for Bounded Linear Programming with Fuzzy Cost Coefficients

2013 ◽  
pp. 40-63
Author(s):  
Ali Ebrahimnejad ◽  
Seyed Hadi Nasseri ◽  
Sayyed Mehdi Mansourzadeh
Keyword(s):  
Linear Programming ◽  
Auxiliary Problem ◽  
Simplex Algorithm ◽  
Upper Bounds ◽  
Bounded Linear ◽  
Fuzzy Variables ◽  
Decision Variables ◽  
Bounded Variables ◽  
Linear Programming Problems ◽  
Fuzzy Cost

In most practical problems of linear programming problems with fuzzy cost coefficients, some or all variables are restricted to lie within lower and upper bounds. In this paper, the authors propose a new method for solving such problems called the bounded fuzzy primal simplex algorithm. Some researchers used the linear programming problem with fuzzy cost coefficients as an auxiliary problem for solving linear programming with fuzzy variables, but their method is not efficient when the decision variables are bounded variables in the auxiliary problem. In this paper the authors introduce an efficient approach to overcome this shortcoming. The bounded fuzzy primal simplex algorithm starts with a primal feasible basis and moves towards attaining primal optimality while maintaining primal feasibility throughout. This algorithm will be useful for sensitivity analysis using primal simplex tableaus.

Sign in / Sign up

Export Citation Format

Share Document