On another view of an inverse of an interval matrix

We consider n × n matrix whose elements are fuzzy numbers (hereinafter a fuzzy matrix) and we introduce notions of regularity of a fuzzy matrix and the inverse matrix of a fuzzy matrix (hereinafter the fuzzy inverse) in this paper. It is shown that the fuzzy inverse is a fuzzy matrix as well. Also we pay attention to the calculation of the fuzzy inverse in a special case. Main results are based on Rohn’s results in the field of linear problems with inexact data.     

Understanding one another: making out meanings with Boolean equations

Autonomous ai agents raise the issue of semantic interoperability between independently architectured and differently embodied intelligences. This article offers an approach to the issue with certain aspects that are close in spirit to the way humans make out meanings. Using a mathematical model of cognition, it is shown how agents with autonomously developed conceptualizations can bootstrap and unravel each other’s meanings ad hoc. The domain general methodology is based on the agents’ capability to deal with Boolean operations, and on the shared outside environment. No prior provisions are required. The formalized cognitive process consists of constructing, and solving, Boolean equations that are grounded in the shared environment. The process yields a testable conjecture about the grounded conceptual representation of the other, along with a testable conjectured translation that maps from that representation to one’s own.      

An improved algorithm for Boolean matrix multiplication

A new algorithm for computing the product of two arbitraryN×N Boolean matrices is presented. The algorithm requiresO (N ³/logN) bit operations and onlyO(N logN) bits of additional storage. This represents an improvement on the Four Russians' method which requires the same number of operations but usesO(N ³/logN) bits of additional storage.     

Constructing irreducible coverings of a Boolean matrix

A polynomial delay algorithm for searching for irreducible coverings of a Boolean matrix is constructed. A similar result is obtained for the problem of constructing maximal conjunctions of a monotone Boolean function specified by its conjunctive normal form. Elena V. Djukova born 1945. Graduated from Moscow State University in 1967. Candidate’s degree in Physics and Mathematics in 1979. Doctoral degree in Physics and Mathematics in 1997. Dorodnicyn Computing Center, Russian Academy of Sciences, leading researcher. Moscow State University, lecturer. Moscow Pedagogical University, lecturer. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of 76 papers. Andrey S. Inyakin born 1978. Graduated from Moscow State University in 2000. Candidate’s degree in Physics and Mathematics in 2000. Dorodnicyn Computing Center, Russian Academy of Sciences, junior researcher. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of sixteen papers.     

The Boolean column and column-row matrix decompositions

Matrix decompositions are used for many data mining purposes. One of these purposes is to find a concise but interpretable representation of a given data matrix. Different decomposition formulations have been proposed for this task, many of which assume a certain property of the input data (e.g., nonnegativity) and aim at preserving that property in the decomposition. In this paper we propose new decomposition formulations for binary matrices, namely the Boolean CX and CUR decompositions. They are natural combinations of two previously presented decomposition formulations. We consider also two subproblems of these decompositions and present a rigorous theoretical study of the subproblems. We give algorithms for the decompositions and for the subproblems, and study their performance via extensive experimental evaluation. We show that even simple algorithms can give accurate and intuitive decompositions of real data, thus demonstrating the power and usefulness of the proposed decompositions.     

Complexity of monotone networks for Boolean matrix product

Any computation of Boolean matrix product by an acyclic network using only the operations of binary conjunction and disjunction requires at least IJK conjunctions and IJ(K?1) disjunctions for the product of matrices of sizes I×K and K×J. Furthermore any two such networks having these minimum numbers of operations are equivalent using only the commutativity of both operations and the associativity of disjunction.     

A sparse matrix algorithm on the Boolean vector machine

The Boolean Vector Machine (BVM) is a large network of extremely small processors with very small memories operating in SIMD mode using bit serial arithmetic. Individual processors communicate via a hardware implementation of the Cube Connected Cycles (CCC) network. A prototype BVM with 2048 processing elements, each with 200 binary bits of memory, is currently being built using VLSI technology.

The BVM's bit-serial arithmetic and the small memories of individual processors are apparently a drawback to its effectiveness when applied to large numerical problems. In this paper we analyze an implementation of a basic matrix-vector iteration algorithm for sparse matrices on the BVM. We show that a 2²⁰ Pe BVM can deliver over 1 billion (10⁹) useful floating-point operations per second for this problem. The algorithm is expressed in a new language (BVL) which has been defined for programming the BVM.     

基于布尔矩阵约简的Apriori算法改进研究

针对关联规则中Apriori算法存在的缺点,提出了一种基于布尔矩阵约简的Apriori改进算法。在该算法中,将事务数据库转换为布尔矩阵,并在矩阵最后增加1行2列,用来记录相同事务的个数和矩阵行与列中"1"的个数。将矩阵各列元素按支持数升序排列,使得算法在压缩过程中减少了扫描矩阵各列的次数,缩短了算法的运行时间。另外,为了提高算法的存储空间利用率,增加了删除非频繁项集的操作。实验结果和性能分析表明,相比现有的算法,改进后的算法具有更好的性能,能够有效地提高算法执行效率。     

基于布尔矩阵的关联规则算法研究*

针对可快速在大型交易事务数据库中挖掘关联规则的问题,基于布尔矩阵提出一种新的挖掘算法。该算法通过仅需存储布尔位节约了内存,通过简单布尔运算提高了求解频繁项集的效率。实验证明该算法较之于Apriori 算法有更好的性能。     

决策表中粗糙集的布尔矩阵表示

建立了布尔矩阵与逻辑方程组的解和决策表中的属性集之间的关系;然后在此基础上给出了决策表中的粗糙集理论的布尔矩阵表示;最后证明了属性约简在布尔矩阵和代数两种不同表示下是等价的。这些结论有助于人们深刻理解粗糙集理论的本质,同时为寻找高效的属性约简算法奠定了基础。     

基于布尔矩阵的区间数排序方法

讨论了10 个区间数排序的可能度公式, 分析了它们各自的特点. 从可能度的含义和保序性两个角度指出, 基于可能度矩阵的区间数排序方法有时会导出不合理的排序结果. 通过分析可能度矩阵与模糊判断矩阵的关系, 剖 析了导致这种不合理排序结果的原因. 最后, 利用可能度矩阵构造一个布尔矩阵, 基于布尔矩阵给出一个改进的区间 数排序算法, 并从理论上证明了所提出的排序方法的科学性.

     

改进的布尔冲突矩阵的高效属性约简算法

近年来,诸多学者喜欢用差别矩阵的方法来设计属性约简的算法,但由于计算差别矩阵不仅费时且还浪费空间,导致这些属性约简算法都不够理想。为了降低属性约简算法的复杂度,在布尔冲突矩阵的基础上,定义了一个启发函数,该函数能求出决策表中条件属性导致的冲突个数,同时给出了计算该启发函数的快速算法。然后用该启发函数设计了一个有效的基于改进的布尔冲突矩阵的不完备决策表的高效属性约简算法,该算法能够有效降低时间复杂度。最后实验结果说明了新算法的有效性。     

Improved time and space bounds for Boolean matrix multiplication

Summary Using modular arithmetic we obtain the following improved bounds on the time and space complexities for n × n Boolean matrix multiplication: O(n ^log ₂ ⁷ lognlogloglognloglogloglogn) bit operations and O(n ²loglog n) bits of storage on a logarithmic cost RAM having no multiply or divide instruction; O(n ^log ₂ ⁷(logn)^2–1/2log ₂ ⁷(loglog n)^1/2log ₂ ^7–1) bit operations and O(n ²log n) bits of storage on a RAM which can use indirect addressing for table lookups. The first algorithm can be realized as a Boolean circuit with O(n ^log ₂ ⁷lognlogloglognloglogloglogn) gates. Whenever n×n arithmetic matrix multiplication can be performed in less than O(n ^log ₂ ⁷) arithmetic operations, our results have corresponding improvements.This work was supported in part by the Office of Naval Research under contract N00014-67-0204-0063, by the National Research Council of Canada under grant A4307, and by the National Science Foundation under grants MCS76-17321 and GJ-43332     

布尔矩阵乘的分布式异构并行优化

布尔多项式求解是当今密码代数分析中的关键步骤,F4算法是布尔多项式求解的高效算法。分析了Lachartre为F4矩阵专门设计的高斯消去算法,针对其中布尔矩阵乘这一耗时的计算步骤,设计并实现了分布式异构(CPU+MIC)并行算法。布尔矩阵相对于普通矩阵主要体现在矩阵元素取值区间不一样上,由于布尔矩阵元素(0,1)导致矩阵乘操作的特殊性,普通矩阵乘的优化方法不能很好地满足布尔矩阵乘的需求。分别从布尔矩阵的存储、OpenMP多线程组织、访存、任务划分和调度等方面进行了性能优化,实现了布尔矩阵乘的分布式异构并行算法。通过随机生成布尔矩阵测试,优化后的分布式异构并行程序相较于分布式同构并行程序达到了2.45的加速比,体现了良好的性能提升。     

多变量密码体制下大型布尔矩阵生成算法

大型可逆布尔矩阵在多变量公钥密码体制设计中有着其广泛用途,而高效的大型可逆布尔矩阵生成算法直接影响多变量公钥密码体制设计的质量。利用线性变换的思想,提出了布尔矩阵B_8n*12n的一种生成算法,具有简单有效的优点。并给出了该大型布尔矩阵生成算法的具体描述,分析了该算法的时间复杂度,密钥的存储空间。整个求解过程和结果表明该算法的有效性。最后给出了其逆矩阵的求解算法。     

On the equal-weight symmetric Boolean functions

Two important classes of symmetric Boolean functions are the equal-weight Boolean functions and the elementary (or homogeneous) symmetric Boolean functions. In this paper we studied the equal-weight symmetric Boolean functions. First the Walsh spectra of the equal-weight symmetric Boolean functions are given. Second the sufficient and necessary condition on correlation-immunity of the equal-weight symmetric Boolean function is derived and other cryptology properties such as the nonlinearity, balance and propagation criterion are taken into account. In particular, the nonlinearity of the equal-weight symmetric Boolean functions with n (n ≥ 10) variables is determined by their Hamming weight. Considering these properties will be helpful in further investigations of symmetric Boolean functions.     

On detecting faults for Boolean expressions

Fault based testing aims at detecting hypothesized faults based on specifications or program source. There are some fault based techniques for testing Boolean expressions which are commonly used to model conditions in specifications as well as logical decisions in program source. The MUMCUT strategy has been proposed to generate test cases from Boolean expressions. Moreover, it detects eight common types of hypothesized faults provided that the original expression is in irredundant disjunctive normal form, IDNF. Software practitioners are more likely to write the conditions and logical decisions in general form rather than IDNF. Hence, it is interesting to investigate the fault detecting capability of the MUMCUT strategy with respect to general form Boolean expressions. In this article, we perform empirical studies to investigate the fault detection capability of the MUMCUT strategy with respect to general form Boolean expressions as well as mutated expressions. A mutated expression can be obtained from the original given Boolean expression by making a syntactic change based on a particular type of fault.