MapReduce for Integer Factorization
نویسنده
چکیده
Integer factorization is a very hard computational problem. Currently no e cient algorithm for integer factorization is publicly known. However, this is an important problem on which it relies the security of many real world cryptographic systems. I present an implementation of a fast factorization algorithm on MapReduce. MapReduce is a programming model for high performance applications developed originally at Google. The quadratic sieve algorithm is split into the di erent MapReduce phases and compared against a standard implementation.
منابع مشابه
On the WZ Factorization of the Real and Integer Matrices
The textit{QIF} (Quadrant Interlocking Factorization) method of Evans and Hatzopoulos solves linear equation systems using textit{WZ} factorization. The WZ factorization can be faster than the textit{LU} factorization because, it performs the simultaneous evaluation of two columns or two rows. Here, we present a method for computing the real and integer textit{WZ} and textit{ZW} factoriz...
متن کاملDocument Clustering Through Non-Negative Matrix Factorization: A Case Study of Hadoop for Computational Time Reduction of Large Scale Documents
In this paper we discuss a new model for document clustering which has been adapted using non-negative matrix factorization method. The key idea is to cluster the documents after measuring the proximity of the documents with the extracted features. The extracted features are considered as the final cluster labels and clustering is done using cosine similarity which is equivalent to k-means with...
متن کاملCollaborative Filtering Recommendation using Matrix Factorization: A MapReduce Implementation
Matrix Factorization based Collaborative Filtering (MFCF) has been an efficient method for recommendation. However, recent years have witness the explosive increasing of big data, which contributes to the huge size of users and items in recommender systems. To deal with the efficiency of MFCF recommendation in the context of big data challenge, we propose to leverage MapReduce programming model...
متن کاملSimilar User Index-based MapReduce for Distributed Recommender Systems
Due to the time complexity in composing recommendations, matrix factorization-based approaches are inefficient in dealing with large scale datasets. In this paper, we propose a similar user indexbased parallel matrix factorization approach. Since the group of similar users is indexed in advance, there is no need to compute similarities between all users in datasets. Furthermore, the size of a m...
متن کاملDistributed Flexible Nonlinear Tensor Factorization
Tensor factorization is a powerful tool to analyse multi-way data. Compared with traditional multi-linear methods, nonlinear tensor factorization models are capable of capturing more complex relationships in the data. However, they are computationally expensive and may suffer severe learning bias in case of extreme data sparsity. To overcome these limitations, in this paper we propose a distrib...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره abs/1001.0421 شماره
صفحات -
تاریخ انتشار 2009