An approximate algorithm for minimization of weighted depth of decision trees is considered. A bound on accuracy of this algorithm is obtained which is unimprovable in general case. Under some natural assumptions on t...
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An approximate algorithm for minimization of weighted depth of decision trees is considered. A bound on accuracy of this algorithm is obtained which is unimprovable in general case. Under some natural assumptions on the class NP, the considered algorithm is close ( from the point of view of accuracy) to best polynomial approximate algorithms for minimization of weighted depth of decision trees.
We present a greedy algorithm for solving a special class of convex programming problems and establish a connection with polymatroid theory which yields a theoretical explanation and verification of the algorithm via ...
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We present a greedy algorithm for solving a special class of convex programming problems and establish a connection with polymatroid theory which yields a theoretical explanation and verification of the algorithm via some recent results of S. Fujishige.
Information hiding, also known as data hiding, is an emerging field that combines multiple theories and technologies. In recent years, Chang et al. and Liu et al. have proposed new data hiding schemes based on Sudoku,...
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Information hiding, also known as data hiding, is an emerging field that combines multiple theories and technologies. In recent years, Chang et al. and Liu et al. have proposed new data hiding schemes based on Sudoku, a turtle-shell, etc. These proposed schemes have their own advantages in terms of visual quality and embedded capacity. However, the reference matrices used in these schemes are not optimal. Based on the characteristics of these schemes, Jin et al. employed particle swarm optimisation to select the reference matrix and achieved approximately optimal results in reducing the distortion of the stego-image. However, the complexity is high. In this paper, a turtle-shell matrix optimisation scheme is proposed using a greedy algorithm. The experimental results show that our proposed greedy algorithm is better than the particle swarm optimisation scheme at finding a near-optimal matrix and achieving better stego-image quality, and it outperforms the particle swarm optimisation scheme in terms of computational amount and efficiency.
We generalize the well-known greedy approximation algorithm, by allowing gaps in the approximating sequence. We give examples of bases which are "quasi-greedy with gaps," in spite of failing to be quasi gree...
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We generalize the well-known greedy approximation algorithm, by allowing gaps in the approximating sequence. We give examples of bases which are "quasi-greedy with gaps," in spite of failing to be quasi greedy in the usual sense. However, we also show that for some classical bases (such as the normalized Haar basis in L-1, and the trigonometric'basis in L-p for p not equal 2), the greedy algorithm may diverge, even if gaps are introduced into the approximating sequence. (C) 2017 Elsevier Inc. All rights reserved.
We study the following nonlinear method of approximation by trigonometric polynomials in this paper. For a periodic function f we take as an approximant a trigonometric polynomial of the form G(m) (f) := Sigma(k epsil...
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We study the following nonlinear method of approximation by trigonometric polynomials in this paper. For a periodic function f we take as an approximant a trigonometric polynomial of the form G(m) (f) := Sigma(k epsilon Lambda) (f) over cap(k)e(i(k,x)), where Lambda subset of Z(d) is a set of cardinality m containing the indices of the nl biggest (in absolute value) Fourier coefficients (f) over cap(k) of function f. We compare the efficiency of this method with the best m-term trigonometric approximation both for individual functions and for some function classes. It turns out that the operator G(m) provides the optimal (in the sense of order) error of m-term trigonometric approximation in the L-p-norm for many classes.
In this note, we investigate the efficiency of the greedy algorithm for the classes of multivariate periodic functions with low mixed smoothness in L-q with regard to the wavelet-type basis. We find that the order of ...
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In this note, we investigate the efficiency of the greedy algorithm for the classes of multivariate periodic functions with low mixed smoothness in L-q with regard to the wavelet-type basis. We find that the order of greedy approximation in the case of low smoothness is different for some range of parameters. (c) 2005 Elsevier Inc. All rights reserved.
Nonlinear m-term approximation plays an important role in machine learning, signal processing and statistical estimating. In this paper by means of a nondecreasing dominated function, a greedy adaptive compression num...
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Nonlinear m-term approximation plays an important role in machine learning, signal processing and statistical estimating. In this paper by means of a nondecreasing dominated function, a greedy adaptive compression numerical algorithm in the best m -term approximation with regard to tensor product wavelet-type basis is pro-posed. The algorithm provides the asymptotically optimal approximation for the class of periodic functions with mixed Besov smoothness in the L q norm. Moreover, it depends only on the expansion of function f by tensor pro-duct wavelet-type basis, but neither on q nor on any special features of f.
In the paper the accuracy of greedy algorithm for construction of partial tests (superreducts) and partial decision rules is Considered. Results of experiments with greedy algorithm are described.
In the paper the accuracy of greedy algorithm for construction of partial tests (superreducts) and partial decision rules is Considered. Results of experiments with greedy algorithm are described.
The article mainly researches path planning and task allocation problems of multiple mobile robots using A* searching algorithm and greedy algorithm, and solve the shortest path problems such that the robots can move ...
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ISBN:
(纸本)9783037850046
The article mainly researches path planning and task allocation problems of multiple mobile robots using A* searching algorithm and greedy algorithm, and solve the shortest path problems such that the robots can move from the start point to reach the multiple target points in a collision-free space, and uses 2-opt exchange heuristic algorithm to improve the shortest path. In this manner, the mobile moves to the final target point through the other points, and construct the motion path using A* searching algorithm and greedy algorithm. The supervised computer control the mobile robot feedback to the start point from the final target point through the other points, and programs a shortest path using 2-opt exchange heuristic algorithm. We develop the user interface to program the motion path of mobile robots via wireless RF interface. It can displays the motion path of the mobile robot on real-time. The simulated results presents that the proposed method can finds the shortest motion path for mobile robots moving to multiple target points from the start point in a collision-free space. Finally, we implement the experiment scenario on the grid platform using the module-based mobile robot.
We study the optimal sampling set selection problem in sampling a noisy k-bandlimited graph signal. To minimize the effect of noise when trying to reconstruct a k-bandlimited graph signal from m samples, the optimal s...
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ISBN:
(纸本)9781538646588
We study the optimal sampling set selection problem in sampling a noisy k-bandlimited graph signal. To minimize the effect of noise when trying to reconstruct a k-bandlimited graph signal from m samples, the optimal sampling set selection problem has been shown to be equivalent to finding a m x k submatrix with the maximum smallest singular value, sigma(min) [3]. As the problem is NP-hard, we present a greedy algorithm inspired by a similar submatrix selection problem known in computer science and to which we add a local search refinement. We show that 1) in experiments, our algorithm finds a submatrix with larger sigma(min )than prior greedy algorithm [3], and 2) has a proven worst-case approximation ratio of 1/(1 + epsilon)k, where epsilon is a constant.
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