Query processing for data streams should be continuous and rapid, which requires strict time constraint. In most previous researches, in order to guarantee this constraint, the evaluation order of join predicates in a continuous query is optimized by a greedy. However, the greedy strategy traces only the first promising plan, so that it often finds a sub-optimal plan. This paper proposes an imp...

We shall show that if the restricted isometry constant (RIC) δs+1(A) of the measurement matrix A satisfies δs+1(A) < 1 √ s+ 1 , then the greedy algorithm Orthogonal Matching Pursuit(OMP) will succeed. That is, OMP can recover every s-sparse signal x in s iterations from b = Ax. Moreover, we shall show the upper bound of RIC is sharp in the following sense. For any given s ∈ N, we shall construc...

The CORDIC algorithm is a well-known iterative method for the computation of vector rotation. For applications that require forward rotation (or vector rotation) only, the Extended Elementary Angle Set (EEAS) Scheme provides a relaxed approach to speed up the operation of the CORDIC algorithm. When determining the parameters of EEAS-based CORDIC algorithm, two optimization problems are encounte...

In this paper we consider the task of estimating the non-zero pattern of the sparse inverse covariance matrix of a zero-mean Gaussian random vector from a set of iid samples. Note that this is also equivalent to recovering the underlying graph structure of a sparse Gaussian Markov Random Field (GMRF). We present two novel greedy approaches to solving this problem. The first estimates the non-ze...

We consider causal structure learning from observational data. The main existing approaches can be classified as constraint-based, score-based and hybrid methods, where the latter combine aspects of both constraint-based and score-based approaches. Hybrid methods often apply a greedy search on a restricted search space, where the restricted space is estimated using a constraint-based method. Th...

Borodin, Nielsen and Rackoff [5] proposed a framework for abstracting the main properties of greedy-like algorithms with emphasis on scheduling problems, and Davis and Impagliazzo [6] extended it so as to make it applicable to graph optimization problems. In this paper we propose a related model which places certain reasonable restrictions on the power of the greedy-like algorithm. Our goal is ...

We expand the item response theory to study the case of “cheating students” for a set of exams, trying to detect them by applying a greedy algorithm of inference. This extended model is closely related to the Boltzmann machine learning. In this paper we aim to infer the correct biases and interactions of our model by considering a relatively small number of sets of training data. Nevertheless, ...

The performance of acquisition functions for Bayesian optimisation to locate the global optimum continuous is investigated in terms Pareto front between exploration and exploitation. We show that Expected Improvement (EI) Upper Confidence Bound (UCB) always select solutions be expensively evaluated on front, but Probability not guaranteed do so Weighted does only a restricted range weights. int...

In the context of compressed sensing (CS), both Subspace Pursuit (SP) and Compressive Sampling Matching Pursuit (CoSaMP) are very important iterative greedy recovery algorithms which could reduce the recovery complexity greatly comparing with the well-known l1-minimization. Restricted isometry property (RIP) and restricted isometry constant (RIC) of measurement matrices which ensure the converg...