Energy-Efficient Algorithms

Research on sorting algorithms is a very core research field in computer science. Today, any advancement in sorting algorithms would lead to less time and space required to perform the sorting task. This paper discusses a newly proposed algorithm—‘ledger-based sorting algorithm’. It is basically an advancement to ‘counting sort’, but not exactly counting sort. The purpose of this paper is to reduce the time and space required for sorting when the numbers given to you belong to a particular range (0–n) and all of them are distinct. This paper also compares the newly proposed algorithm with insertion sort, bubble sort, selection sort, quick sort, merge sort and mainly counting sort through graphical time analysis of these algorithms. The paper especially compares counting sort algorithm with the proposed algorithm in terms of both time and space complexity.

This paper focuses on Dynamic Implementation using Array and Linked list. In the data structure concept, the list plays a major role in the allocation of data where it is a general overview of creating a node in a linked list using an array with the initialization of size. The node contains data and is interconnected to the next node in the sequence via their links which perform the search operation where the space and time complexity are not wasted in these analyses. Linked lists are a great way to store a theoretically infinite amount of data with a small and versatile amount of code. The amount of data stored increases linearly with the number of nodes in the list (space-o(n)) where linked lists are more efficient than arrays. It is observed that considering the time complexity when there is a variation in data set linked array is almost reaches close to array whenever dataset is high also linked array has better time complexity than linked list when the searching element is in middle and last. Considering the space complexity linked array is almost 2 times and 4 times less space is needed than array and linked list respectively.

The classic buffer insertion algorithm of van Ginneken has time and space complexity O(n/sup 2/), where n is the number of possible buffer positions. For more than a decade, van Ginneken's algorithm has been the foundation of buffer insertion. In this paper, we present a new algorithm that computes the same optimal buffer insertion, but runs much faster. For 2-pin nets, our time complexity is O(nlogn) and space complexity is O(n). For multipin nets, our time complexity is O(nlog/sup 2/n) and space complexity is O(nlogn). The speedup is achieved by four novel techniques: predictive pruning, candidate tree, fast redundancy check, and fast merging. On industrial test cases, the new algorithms is 2-80 times faster than van Ginneken's algorithm and uses 1/4-1/500 of the memory. Since van Ginneken's algorithm and its variations are used by most existing algorithms on buffer insertion and buffer sizing, our new algorithm significantly improves the performance of all these algorithms. The predictive pruning technique has been applied to buffer cost minimization (Shi et al., 2004), and significantly improved the running time.

Binary embedding refers to methods for embedding points in $\mathbb {R}^{d}$ into vertices in the Hamming cube of dimension ${\mathcal{O}}(d)$ , such that the normalized Hamming distance between the codes preserves a prespecified distance between vectors in the original space. A common approach to binary embedding is to use random projection , followed by one-bit quantization to produce binary codes. Of particular interest, in this letter, is sparse circulant binary embedding (SCBE), where a sparse random circulant matrix with random sampling at a rate of $1-s$ for $s\in (0, 1)$ is used for random projection. The SCBE has the space complexity ${\mathcal{O}}((1-s)d)$ , while unstructured random projection has the space complexity ${\mathcal{O}}(d^2)$ . We present an asymptotic analysis of SCBE, when $d$ approaches $\infty$ , showing that the performance of SCBE is comparable to that of binary embedding with unstructured random projection whilethe former has the space complexity ${\mathcal{O}}((1-s)d)$ and the time complexity ${\mathcal{O}}(d\log d)$ but the latter has both space and time complexities ${\mathcal{O}}(d^2)$ .

WITHDRAWN: Spearman rank correlation based bagging ensemble bayes optimal classification for genomic predictive pattern analytics

This paper introduces a new heuristic function with high efficiency for an optimum solving of the 8 - puzzle, this one being the double of the Chebyshev distance. The comparative study is realized among this new heuristic (Chebyshev heuristic), the Hamming h euristic and the Manhattan heuristic using A* algorithm implemented in Java. The Chebyshev heuristic function is more informed than Hamming and Manhattan heuristics. This paper also presents the necessary stages in object oriented development of an interactive software dedicated to simulate the A* search algorithm for 8 -puzzle. The modeling of the software is achieved through specific UML diagrams representing the phases of analysis, design and implementation, the system thus being described in a clear and practical manner. In order to confirm the obtained theoretical results which show that Chebyshev heuristic is more efficient, two performance criteria were used: space complexity and time complexity. The space complexity was measured by the number of generated nodes from the search tree, by the number of the expanded nodes and by the effective branching factor. The time complexity was measured by the running time. From the experimental results obtained by using the Chebyshev heuristic, improvements were obs erved regarding space and time complexity of A* algorithm versus Hamming and Manhattan heuristics.

A faster FPTAS for knapsack problem with cardinality constraint

A number of algorithms are available for computing the simulation relation on Kripke structures and on labelled transition systems representing concurrentsystems. Among them, the algorithm by Ranzato and Tapparo~[2007] has the best time complexity, while the algorithm by Gentilini et al.~[2003]~--~successivelycorrected by van Glabbeek and Ploeger~[2008]~--~has thebest space complexity. Both space and time complexities are critical issues in a simulation algorithm, in particular memory requirements are crucial in the context of model checking when dealing with large state spaces.We propose here a new simulation algorithm thatis obtained as a space saving modification of the time efficient algorithm by Ranzato and Tapparo: a symbolic representation of sets is embedded in thisalgorithm so that any set of states manipulated by the algorithm can be efficiently stored as a set of blocks of a suitable state partition. It turns out that this new simulation algorithm retains a space complexity comparable with Gentilini et al.'s algorithm while improving on Gentilini et al.'s time bound.

Shellsort is a comparison sort that uses insertion sort at each iteration to make a list of interleaved elements nearly sorted so that at the last iteration the list is almost sorted. The time complexity of Shellsort is dependent upon the method of interleaving (called increment sequence) giving variants of Shellsort. However, the problem of finding proper interleaving to achieve the minimum time complexity of O(n log n) is still open. In this paper, we have analyzed the performance of variants of Shellsort based on their time complexity. Our measure of time complexity is independent of the machine configuration and considers all the operations of a sorting process. We found that the interleaving method or increment sequence proposed by Sedgewick performs best among the analyzed variants.

This paper proposes a novel learning algorithm- SVM based MLP neural network algorithm (SVMMLP), which based on the Maximal Margin (MM) principle and take into account the idea of support vectors. SVMMLP has time and space complexities O(N) while usual SVM training methods have time complexity O(N3) and space complexity O(N2), where N is the training-dataset size. Intrusion detection benchmark datasets – NSL-KDD used in experiments that enable a comparison with other state-of-the-art classifiers. The results provide evidence of the effectiveness of our methods regarding accuracy, AUC, and Balanced Error Rate (BER).KeywordsMLP neural networkconvergenceSVMMLPintrusion detection

We present an in-place depth first search (DFS) and an in-place breadth first search (BFS) that runs on a word RAM in linear time such that, if the adjacency arrays of the input graph are given in a sorted order, the input is restored after running the algorithm. To obtain our results we use properties of the representation used to store the given graph and show several linear-time in-place graph transformations from one representation into another.

Kernel ridge regression (KRR) is a powerful method for nonparametric regression. The time and space complexity of computing the KRR estimate directly are O(n3) and O(n2) , respectively, which are prohibitive for large-scale data sets, where n is the number of data. In this article, we propose a novel random sketch technique based on the circulant matrix that achieves savings in storage space and accelerates the solution of the KRR approximation. The circulant matrix has the following advantages: It can save time complexity by using the fast Fourier transform (FFT) to compute the product of matrix and vector, its space complexity is linear, and the circulant matrix, whose entries in the first column are independent of each other and obey the Gaussian distribution, is almost as effective as the i.i.d. Gaussian random matrix for approximating KRR. Combining the characteristics of the circulant matrix and our careful design, theoretical analysis and experimental results demonstrate that our proposed sketch method, making the estimate kernel methods scalable and practical for large-scale data problems, outperforms the state-of-the-art KRR estimates in time complexity while retaining similar accuracies. Meanwhile, our sketch method provides the theoretical bound that keeps the optimal convergence rate for approximating KRR.

Routing with multiple quality-of-services constraints: An approximation perspective

The precision and efficiency of the similarity computing of documents is the foundation and key of other documents processing. In this paper, the DF and TF-IDF algorithms are improved. First, DF's time complexity is linear which suits mass documents processing, but it has the fault that exceptional useful features may be deleted, so we make up that by adding the count of the words at the important places. Second, we rectify the weight of feature by the result of feature selection phase. In this way, we improve the precision of documents similarity without adding much time and space complexity.

In this paper a fast and space efficient method for simulating a d′-dimensional cellular automaton (CA) on a d-dimensional CA (d<d′) is introduced. For d′=2 and d=1 this method is optimal (under certain assumptions) with respect to time as well as space complexity. Let in this case t(n) be the time complexity and r(n) the side length of the smallest square enclosing all used cells. Then the simulation does not need more than O(r 2) cells and has a running time of d′ = d+1 a version with the time and space complexity of O(t 2) will be presented. Finally it will be shown, how it is possible to simulate a 2d-dimensional CA on a d-dimensional CA in a similarly efficient way.KeywordsTime ComplexityCellular AutomatonSpace ComplexityQuiescent StateComputation StepThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.