A discrete time model for software reliability with application to a flight control software

This work is motivated by a particular software reliability problem in a unit of flight control software developed by the Indian Space Research Organization (ISRO), in which the testing of the software is carried out in multiple batches, each consisting of several runs. As the errors are found during the runs within a batch, they are noted, but not debugged immediately; they are debugged only at the end of that particular batch of runs. In this work, we introduce a discrete time model suitable for this type of periodic debugging schedule and describe maximum likelihood estimation for the model parameters. This model is used to estimate the reliability of the software. We also develop a method to determine the additional number of error‐free test runs required for the estimated reliability to achieve a specific target with some high probability. We analyze the test data on the flight control software of ISRO. Copyright © 2011 John Wiley & Sons, Ltd.     

A fourth‐order H1‐Galerkin mixed finite element method for Kuramoto–Sivashinsky equation

A H¹‐Galerkin mixed finite element method is applied to the Kuramoto–Sivashinsky equation by using a splitting technique, which results in a coupled system. The method described in this article may also be considered as a Petrov–Galerkin method with cubic spline space as trial space and piecewise linear space as test space, since the second derivative of a cubic spline is a linear spline. Optimal‐order error estimates are obtained without any restriction on the mesh for both semi‐discrete and fully discrete schemes. The advantage of this method over that presented in Manickam et al., Comput. Math. Appl. vol. 35(6) (1998) pp. 5–25; for the same problem is that the size (i.e., (n + 1) × (n + 1)) of each resulting linear system is less than half of the size of the linear system of the earlier method, where n is the number of subintervals in the partition. Further, there is a requirement of less regularity on exact solution in this method. The results are validated with numerical examples. Finally, instability behavior of the solution is numerically captured with this method.     

Continuum limits for classical sequential growth models

A random graph order, also known as a transitive percolation process, is defined by taking a random graph on the vertex set {0,…,n ? 1} and putting i below j if there is a path i = i₁…i_k = j in the graph with i₁ < … < i_k. Rideout and Sorkin 14 provide computational evidence that suitably normalized sequences of random graph orders have a “continuum limit.” We confirm that this is the case and show that the continuum limit is always a semiorder. Transitive percolation processes are a special case of a more general class called classical sequential growth models. We give a number of results describing the large‐scale structure of a general classical sequential growth model. We show that for any sufficiently large n, and any classical sequential growth model, there is a semiorder S on {0,…,n ‐ 1} such that the random partial order on {0,…,n ‐ 1} generated according to the model differs from S on an arbitrarily small proportion of pairs. We also show that, if any sequence of classical sequential growth models has a continuum limit, then this limit is (essentially) a semiorder. We give some examples of continuum limits that can occur. Classical sequential growth models were introduced as the only models satisfying certain properties making them suitable as discrete models for spacetime. Our results indicate that this class of models does not contain any that are good approximations to Minkowski space in any dimension ≥ 2. © 2009 Wiley Periodicals, Inc. Random Struct. Alg., 2010     

Testing Homotopy for Paths in the Plane

In this paper we present an efficient algorithm to test if two given paths are homotopic; that is, whether they wind around obstacles in the plane in the same way. For paths specified by n line segments with obstacles described by n points, several standard ways achieve quadratic running time. For simple paths, our algorithm runs in O(n log n) time, which we show is tight. For self-intersecting paths the problem is related to Hopcrofts problem; our algorithm runs in O(n ^3/2log n) time.     

An exact test of the accuracy of binary classification models based on the probability distribution of the average rank

We propose a new way to evaluate the discriminatory power of models that generate a continuous value as the basis for performing a binary classification task. Our hypothesis test uses the average rank of the k successes in the sample of size n, based on those continuous values. We derive the probability mass function for the average rank from the coefficients of a Gaussian polynomial distribution that results from randomly sampling k distinct positive integers, all n or less. The significance level of the test is found by counting the number of arrangements that produce average ranks more extreme than the one observed. Recursive relationships can be used to calculate the values necessary to compute the p-value. For large values of k and n, for which exact computation might be prohibitive, we present numerical results which indicate that the critical values of the distribution are nearly linear in n for a fixed k and that the coefficients of the linear relationships are nonlinear functions of k and the desired percentile. We develop regression models for those relationships to approximate the number of arrangements in order to make the test practical for large values of k and n.     

Covariate Selection in High-Dimensional Generalized Linear Models With Measurement Error

In many problems involving generalized linear models, the covariates are subject to measurement error. When the number of covariates p exceeds the sample size n, regularized methods like the lasso or Dantzig selector are required. Several recent papers have studied methods which correct for measurement error in the lasso or Dantzig selector for linear models in the p > n setting. We study a correction for generalized linear models, based on Rosenbaum and Tsybakov’s matrix uncertainty selector. By not requiring an estimate of the measurement error covariance matrix, this generalized matrix uncertainty selector has a great practical advantage in problems involving high-dimensional data. We further derive an alternative method based on the lasso, and develop efficient algorithms for both methods. In our simulation studies of logistic and Poisson regression with measurement error, the proposed methods outperform the standard lasso and Dantzig selector with respect to covariate selection, by reducing the number of false positives considerably. We also consider classification of patients on the basis of gene expression data with noisy measurements. Supplementary materials for this article are available online.     

Principal forms <Emphasis Type="Italic">X</Emphasis><Superscript>2</Superscript>+<Emphasis Type="Italic">nY</Emphasis><Superscript>2</Superscript> representing many integers

In 1966, Shanks and Schmid investigated the asymptotic behavior of the number of positive integers less than or equal to x which are represented by the quadratic form X ²+nY ². Based on some numerical computations, they observed that the constant occurring in the main term appears to be the largest for n=2. In this paper, we prove that in fact this constant is unbounded as n runs through positive integers with a fixed number of prime divisors.     

Long repetitive patterns in random sequences

Summary Appearances of long repetitive sequences such as 00...0 or 1010...101 in random sequences are studied. The expected length of the longest repetitive run of any specified type in a random binary sequence of length n is shown to tend to the binary logarithm of n plus a periodic function of log n. Necessary and sufficient conditions are derived to ensure that with probability 1 an infinite random sequence should contain repetitive runs of specified lengths in given initial segments. Finally, the number of long repetitive runs of a specified kind that occur in a random sequence is studied. These results are derived from simple expressions for the generating functions for the probabilities of occurrences of various repetitive runs. These generating functions are rational, and lead to sharp asymptotic estimates for the probabilities.     

THE NET REPRODUCTIVE VALUE AND STABILITY IN MATRIX POPULATION MODELS

The net reproductive value n is defined for a general discrete linear population model with a non-negative projection matrix. This number is shown to have the biological interpretation of the expected number of offspring per individual over its life time. The main result relates n to the population's growth rate (i.e. the dominant eigenvalue λ of the projection matrix) and shows that the stability of the extinction state (the trivial equilibrium) can be determined by whether n is less than or greater than 1. Examples are given to show that explicit algebraic formulas for n are often derivable, and hence available for both numerical and parameter studies of stability, when no such formulas for λ are available.     

Compound Poisson Approximation for Multiple Runs in a Markov Chain

We consider a sequence X ₁, ..., X _n of r.v.'s generated by a stationary Markov chain with state space A = {0, 1, ..., r}, r 1. We study the overlapping appearances of runs of k _i consecutive i's, for all i = 1, ..., r, in the sequence X ₁,..., X _n. We prove that the number of overlapping appearances of the above multiple runs can be approximated by a Compound Poisson r.v. with compounding distribution a mixture of geometric distributions. As an application of the previous result, we introduce a specific Multiple-failure mode reliability system with Markov dependent components, and provide lower and upper bounds for the reliability of the system.     

Pseudorandom generators for space-bounded computation

Pseudorandom generators are constructed which convertO(SlogR) truly random bits toR bits that appear random to any algorithm that runs inSPACE(S). In particular, any randomized polynomial time algorithm that runs in spaceS can be simulated using onlyO(Slogn) random bits. An application of these generators is an explicit construction of universal traversal sequences (for arbitrary graphs) of lengthn ^O(logn).The generators constructed are technically stronger than just appearing random to spacebounded machines, and have several other applications. In particular, applications are given for deterministic amplification (i.e. reducing the probability of error of randomized algorithms), as well as generalizations of it.This work was done in the Laboratory for Computer Science, MIT, supported by NSF 865727-CCR and ARO DALL03-86-K-017     

On New Multivariate Probability Distributions and Stochastic Processes with System Reliability and Maintenance Applications

Two distinct methods for construction of some interesting new classes of multivariate probability densities are described and applied. As common results of both procedures three n-variate pdf classes are obtained. These classes are considered as generalizations of the class of univariate Weibullian, gamma, and multivariate normal pdfs. An example of an application of the obtained n-variate pdfs to the problem of modeling the reliability of multicomponent systems with stochastically dependent life-times of their components is given. Obtaining sequences over n = 2, 3, ... of consistent n-variate pdfs, that obey a relatively simple common pattern, for each n, allows us to extend some of the constructions from random vectors to discrete time stochastic processes. Application of one, so obtained, class of highly non-Markovian, but still sufficiently simple, stochastic processes for modeling maintenance of systems with repair, is presented. These models allow us to describe and analyze repaired systems with histories of all past repairs.      

Approximation Algorithms for Min–Max Tree Partition

We consider the problem of partitioning the node set of a graph intopequal sized subsets. The objective is to minimize the maximum length, over these subsets, of a minimum spanning tree. We show that no polynomial algorithm with bounded error ratio can be given for the problem unless P = NP. We present anO(n²) time algorithm for the problem, wherenis the number of nodes in the graph. Assuming that the edge lengths satisfy the triangle inequality, its error ratio is at most 2p − 1. We also present an improved algorithm that obtains as an input a positive integerx. It runs inO(2^{(p + x)p}n²) time, and its error ratio is at most (2 − x/(x + p − 1))p.     

Fitting enclosing cylinders to data in R n

A simple iterative algorithm is given for finding a stationary point of the (non-convex) problem of finding the smallest enclosing (n–d)-cylinder to discrete data in R ⁿ , that is a cylinder whose axis is a d-dimensional linear manifold. An important special case is the problem of finding the smallest enclosing (usual) cylinder, when n=3 and d=1. The method is based on the solution of a sequence of second order cone programming problems, which can be efficiently solved by interior point methods and for which good software packages are available.     

Minimizing Maximum Weighted Error for Imprecise Computation Tasks

We consider the problem of preemptively scheduling a set of imprecise computation tasks on m ≥ 1 identical processors, with each task T_i having two weights, w_i and w′_i. Two performance metrics are considered: (1) the maximum w′-weighted error; (2) the total w-weighted error subject to the constraint that the maximum w′-weighted error is minimized. For the problem of minimizing the maximum w′-weighted error, we give an algorithm which runs in O(n³ log²n) time for multiprocessors and O(n²) time for a single processor. For the problem of minimizing the total w-weighted error subject to the constraint that the maximum w′-weighted error is minimized, we give an algorithm which also has the same time complexity.     

Randomness of the square root of 2 and the Giant Leap,Part 1

We prove that the “quadratic irrational rotation” exhibits a central limit theorem. More precisely, let α be an arbitrary real root of a quadratic equation with integer coefficients; say, α = $ \sqrt 2 $ \sqrt 2 . Given any rational number 0 < x < 1 (say, x = 1/2) and any positive integer n, we count the number of elements of the sequence α, 2α, 3α, …, nα modulo 1 that fall into the subinterval [0, x]. We prove that this counting number satisfies a central limit theorem in the following sense. First, we subtract the “expected number” nx from the counting number, and study the typical fluctuation of this difference as n runs in a long interval 1 ≤ n ≤ N. Depending on α and x, we may need an extra additive correction of constant times logarithm of N; furthermore, what we always need is a multiplicative correction: division by (another) constant times square root of logarithm of N. If N is large, the distribution of this renormalized counting number, as n runs in 1 ≤ n ≤ N, is very close to the standard normal distribution (bell shaped curve), and the corresponding error term tends to zero as N tends to infinity. This is the main result of the paper (see Theorem 1.1). The proof is rather complicated and long; it has many interesting detours and byproducts. For example, the exact determination of the key constant factors (in the additive and multiplicative norming), which depend on α and x, requires surprisingly deep algebraic tools such as Dedeking sums, the class number of quadratic fields, and generalized class number formulas. The crucial property of a quadratic irrational is the periodicity of its continued fraction. Periodicity means self-similarity, which leads us to Markov chains: our basic probabilistic tool to prove the central limit theorem. We also use a lot of Fourier analysis. Finally, I just mention one byproduct of this research: we solve an old problem of Hardy and Littlewood on diophantine sums.     

Matching parentheses in parallel

Parallel algorithms for evaluating arithmetic expressions generally assume the computation tree form to be at hand. The computation tree form can be generated within the same resource bounds as the parenthesis matching problem can be solved. We provide a new cost optimal parallel algorithm for the latter problem, which runs in time O(log n) using O(n/log n) processors on an . We also prove that the algorithm is the fastest possible independently of the number of processors available.     

A capacity-rounding algorithm for the minimum-cost circulation problem: A dual framework of the Tardos algorithm

Recently, É. Tardos gave a strongly polynomial algorithm for the minimum-cost circulation problem and solved the open problem posed in 1972 by J. Edmonds and R.M. Karp. Her algorithm runs in O(m ² T(m, n) logm) time, wherem is the number of arcs,n is the number of vertices, andT(m, n) is the time required for solving a maximum flow problem in a network withm arcs andn vertices. In the present paper, taking an approach that is a dual of Tardos's, we also give a strongly polynomial algorithm for the minimum-cost circulation problem. Our algorithm runs in O(m ² S(m, n) logm) time and reduces the computational complexity, whereS(m, n) is the time required for solving a shortest path problem with a fixed origin in a network withm arcs,n vertices, and a nonnegative arc length function. The complexity is the same as that of Orlin's algorithm, recently developed by efficiently implementing the Edmonds-Karp scaling algorithm.     

The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differences

Various applications involve assigning discrete label values to a collection of objects based on some pairwise noisy data. Due to the discrete—and hence nonconvex—structure of the problem, computing the optimal assignment (e.g., maximum‐likelihood assignment) becomes intractable at first sight. This paper makes progress towards efficient computation by focusing on a concrete joint alignment problem; that is, the problem of recovering n discrete variables x_i ∊ {1, …, m}, 1 ≤ i ≤ n, given noisy observations of their modulo differences {x_i — x_j mod m}. We propose a low‐complexity and model‐free nonconvex procedure, which operates in a lifted space by representing distinct label values in orthogonal directions and attempts to optimize quadratic functions over hypercubes. Starting with a first guess computed via a spectral method, the algorithm successively refines the iterates via projected power iterations. We prove that for a broad class of statistical models, the proposed projected power method makes no error—and hence converges to the maximum‐likelihood estimate—in a suitable regime. Numerical experiments have been carried out on both synthetic and real data to demonstrate the practicality of our algorithm. We expect this algorithmic framework to be effective for a broad range of discrete assignment problems.© 2018 Wiley Periodicals, Inc.     

Bounds for the symmetric 2-peripatetic salesman problem

The 2-Peripatetic Salesman Problem (2-PSP) minimizes the total length of 2 edge-disjoint Hamil-tonian cycles. This type of problems arises in designing communication or computer networks, or whenever one aims to increase network reliability using disjoint tours. The NP-hardness of the 2-PSP is shown. Lower bound values are obtained by generalizing the 1-tree approach for the TSP to a 2 edge-disjoint 1-trees approach for the 2-PSP. One can construct 2 edge-disjoint 1-trees using a greedy algorithm, into which a partitioning procedure is incorporated that runs O(n ² log n) time. Upper bound solutions are obtained by two heuristics based on a lower bound solution and by a modified Savings heuristic for problems up to 140 cities.