Relevant bibliographies by topics / Factorization system

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Factorization system'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Factorization system"

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Casalino, G., N. Del Buono, and M. Minervini. "Nonnegative Matrix Factorizations Performing Object Detection and Localization." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–19. http://dx.doi.org/10.1155/2012/781987.

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We study the problem of detecting and localizing objects in still, gray-scale images making use of the part-based representation provided by nonnegative matrix factorizations. Nonnegative matrix factorization represents an emerging example of subspace methods, which is able to extract interpretable parts from a set of template image objects and then to additively use them for describing individual objects. In this paper, we present a prototype system based on some nonnegative factorization algorithms, which differ in the additional properties added to the nonnegative representation of data, in order to investigate if any additional constraint produces better results in general object detection via nonnegative matrix factorizations.
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Zheng, Weijian, Fengguang Song, Lan Lin, and Zizhong Chen. "Scaling Up Parallel Computation of Tiled QR Factorizations by a Distributed Scheduling Runtime System and Analytical Modeling." Parallel Processing Letters 28, no. 01 (March 2018): 1850004. http://dx.doi.org/10.1142/s0129626418500044.

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Implementing parallel software for QR factorizations to achieve scalable performance on massively parallel manycore systems requires a comprehensive design that includes algorithm redesign, efficient runtime systems, synchronization and communication reduction, and analytical performance modeling. This paper presents a piece of tiled communication-avoiding QR factorization software that is able to scale efficiently for matrices with general dimensions. We design a tiled communication-avoiding QR factorization algorithm and implement it with a fully distributed dynamic scheduling runtime system to minimize both synchronization and communication. The whole class of communication-avoiding QR factorization algorithms uses an important parameter of D (i.e., the number of domains), whose best solution is still unknown so far and requires manual tuning and empirical searching to find it. To that end, we introduce a simplified analytical performance model to determine an optimal number of domains D[Formula: see text]. The experimental results show that our new parallel implementation is faster than a state-of-the-art multicore-based numerical library by up to 30%, and faster than ScaLAPACK by up to 30 times with thousands of CPU cores. Furthermore, using the new analytical model to predict an optimal number of domains is as competitive as exhaustive searching, and exhibits an average performance difference of 1%.
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More, Tejashree, and Prof Surekha Kohle. "Recommendation System Using Matrix Factorization." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 355–59. http://dx.doi.org/10.22214/ijraset.2022.46615.

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Abstract: In today’s world, people are flooded with a lot of information, and no. of choices are overwhelming. For example, in any online shopping platform such as Amazon, if we search for a particular product, thousands of results appear and it becomes very difficult to select an item from vast pool of options. The growth of digital information and the number of users over the Internet has created a potential problem of information overload. The recommendation system solves this problem by searching through a large volume of data and providing personalized content to the user. This paper describes the introduction to the recommendation system, its three main types – content-based filtering, collaborative filtering, and hybrid filtering, and addresses the data sparsity problem. This paper proposed a collaborative filtering approach using matrix factorization to mitigate the sparsity problem and improve the quality of the recommendation.
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Sadeghi, J., Jalil Naji, and Behnam Pourhassan. "Factorization Method in Oscillator with the Aharonov-Casher System." Advances in Mathematical Physics 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/965694.

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We review the oscillator with Aharonov-Casher system and study some mathematical foundation about factorization method. The factorization method helps us to obtain the energy spectrum and general wave function for the corresponding system in some spin condition. The factorization method leads us to obtain the raising and lowering operators for the Aharonov-Casher system. The corresponding operators give us the generators of the algebra.
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Echi, Othman, Sami Lazaar, and Mohamed Oueld Abdallahi. "On some orthogonal factorization systems." Journal of Algebra and Its Applications 14, no. 08 (April 27, 2015): 1550120. http://dx.doi.org/10.1142/s0219498815501200.

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The orthogonality relation between arrows in the class of all morphisms of a given category C yields a "concrete" antitone Galois connection between the class of all subclasses of morphisms of C. For a class Σ of morphisms of C, we denote by ⊥Σ (resp., Σ⊥) the class of all morphisms f in C such that f ⊥ g (resp., g ⊥ f) for each morphism g in Σ. A couple (Σ, Γ) of classes of morphisms is said to be an (orthogonal) prefactorization system if If, in addition the pfs satisfies then it will be called a dense prefactorization system. A pair [Formula: see text] of classes of morphisms in a category C is called an (orthogonal) factorization system if it is a prefactorization system and each morphism f in C has a factorization f = me, with [Formula: see text] and [Formula: see text]. This paper provides several examples of factorization systems and dense factorization systems in the category Top of topological spaces.
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Even, Valérian, and Marino Gran. "On factorization systems for surjective quandle homomorphisms." Journal of Knot Theory and Its Ramifications 23, no. 11 (October 2014): 1450060. http://dx.doi.org/10.1142/s0218216514500606.

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We study and compare two factorization systems for surjective homomorphisms in the category of quandles. The first one is induced by the adjunction between quandles and trivial quandles, and a precise description of the two classes of morphisms of this factorization system is given. In doing this we observe that a special class of congruences in the category of quandles always permute in the sense of the composition of relations, a fact that opens the way to some new universal algebraic investigations in the category of quandles. The second factorization system is the one discovered by E. Bunch, P. Lofgren, A. Rapp and D. N. Yetter. We conclude with an example showing a difference between these factorization systems.
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Ong, Kyle, Kok-Why Ng, and Su-Cheng Haw. "Neural matrix factorization++ based recommendation system." F1000Research 10 (October 25, 2021): 1079. http://dx.doi.org/10.12688/f1000research.73240.1.

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In recent years, Recommender System (RS) research work has covered a wide variety of Artificial Intelligence techniques, ranging from traditional Matrix Factorization (MF) to complex Deep Neural Networks (DNN). Traditional Collaborative Filtering (CF) recommendation methods such as MF, have limited learning capabilities as it only considers the linear combination between user and item vectors. For learning non-linear relationships, methods like Neural Collaborative Filtering (NCF) incorporate DNN into CF methods. Though, CF methods still suffer from cold start and data sparsity. This paper proposes an improved hybrid-based RS, namely Neural Matrix Factorization++ (NeuMF++), for effectively learning user and item features to improve recommendation accuracy and alleviate cold start and data sparsity. NeuMF++ is proposed by incorporating effective latent representation into NeuMF via Stacked Denoising Autoencoders (SDAE). NeuMF++ can also be seen as the fusion of GMF++ and MLP++. NeuMF is an NCF framework which associates with GMF (Generalized Matrix Factorization) and MLP (Multilayer Perceptrons). NeuMF achieves state-of-the-art results due to the integration of GMF linearity and MLP non-linearity. Concurrently, incorporating latent representations has shown tremendous improvement in GMF and MLP, which result in GMF++ and MLP++. Latent representation obtained through the SDAEs’ latent space allows NeuMF++ to effectively learn user and item features, significantly enhancing its learning capability. However, sharing feature extractions among GMF++ and MLP++ in NeuMF++ might hinder its performance. Hence, allowing GMF++ and MLP++ to learn separate features provides more flexibility and greatly improves its performance. Experiments performed on a real-world dataset have demonstrated that NeuMF++ achieves an outstanding result of a test root-mean-square error of 0.8681. In future work, we can extend NeuMF++ by introducing other auxiliary information like text or images. Different neural network building blocks can also be integrated into NeuMF++ to form a more robust recommendation model.
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Ruchitha, K. Venkata. "Book Recommendation System using Matrix Factorization." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4578–82. http://dx.doi.org/10.22214/ijraset.2021.36025.

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In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.
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Chawla, Tanvi. "FFT Factorization Technique for OFDM System." International Journal of Computer Applications 54, no. 5 (September 25, 2012): 36–40. http://dx.doi.org/10.5120/8564-2161.

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Alqadri, Mowafaq, Haslinda Ibrahim, and Sharmila Karim. "On Cyclic Triple System and Factorization." Journal of Engineering and Applied Sciences 14, no. 21 (October 31, 2019): 7928–33. http://dx.doi.org/10.36478/jeasci.2019.7928.7933.

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Dissertations / Theses on the topic "Factorization system"

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Agagu, Tosin. "Recommendation Approaches Using Context-Aware Coupled Matrix Factorization." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37012.

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In general, recommender systems attempt to estimate user preference based on historical data. A context-aware recommender system attempts to generate better recommendations using contextual information. However, generating recommendations for specific contexts has been challenging because of the difficulties in using contextual information to enhance the capabilities of recommender systems. Several methods have been used to incorporate contextual information into traditional recommendation algorithms. These methods focus on incorporating contextual information to improve general recommendations for users rather than identifying the different context applicable to the user and providing recommendations geared towards those specific contexts. In this thesis, we explore different context-aware recommendation techniques and present our context-aware coupled matrix factorization methods that use matrix factorization for estimating user preference and features in a specific contextual condition. We develop two methods: the first method attaches user preference across multiple contextual conditions, making the assumption that user preference remains the same, but the suitability of items differs across different contextual conditions; i.e., an item might not be suitable for certain conditions. The second method assumes that item suitability remains the same across different contextual conditions but user preference changes. We perform a number of experiments on the last.fm dataset to evaluate our methods. We also compared our work to other context-aware recommendation approaches. Our results show that grouping ratings by context and jointly factorizing with common factors improves prediction accuracy.
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Tabari, Michel, and Rawand Sultani. "A comparison of matrix factorization algorithms for a movie recommender system." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229734.

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Recommendation systems is a growing technique for providing a better user experience for discovering new content on a platform. It can be implemented in many contexts such as Netflix for recommending movies. There are many ways to implement recommendation systems. This paper investigated two of these methods - Weighted Alternating Least Squares and Stochastic Gradient Descent - which fall into the category of matrix factorization and measured their performance in regards to time taken for training, error convergence and prediction quality. To our help we have used TensorFlow, a machine learning framework developed by Google which have been providing us with algorithms, models for training, and testing. The results showed that the Weighted Alternating Least Squares model proved to be better in terms of prediction quality: We also found that the quality of our predictions relied heavily on the model's parameters, since optimal predictions for a model can be found through the correct tuning. We concluded that the choice of model depends heavily on the data set investigated, and that optimal parameters for one model cannot simply be transferred to another model.
Rekommendationssystem används alltmer för att förbättra användarupplevelser. Dessa kan implementeras i många sammanhang som i streamingplattformen Netflix för att rekommendera filmer till sina användare. Det finns många sätt att implementera rekommendationssystem och i denna rapport undersöktes två av dessa metoder - Weighted Alternating Least Squares och Stochastic Gradient Descent - som ligger inom kategorin av matrisfaktorisering och deras diverse prestandamått som träningstid, felkonvergens samt kvalitén på förslagen. Till vår hjälp användes TensorFlow, ett ramverk för maskininlärning som utvecklats av Google som har tillhandahållit oss modeller och algoritmer. Resultatet var att Weighted Alternating Least Squares modellen visade sig vara bättre med avseende på kvalitén på förslagen och vi fann även att kvalitén var starkt beroende av modellens parametrar, då vi fann att optimala förslag för en modell kan hittas genom korrekt justering av dessa parametrar. Vi drog slutsatsen att valet av modell beror på den data som undersöks och att optimala parametrar för en modell inte direkt kan överföras till en annan.
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Winck, Ryder Christian. "Simultaneous control of coupled actuators using singular value decomposition and semi-nonnegative matrix factorization." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45907.

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This thesis considers the application of singular value decomposition (SVD) and semi-nonnegative matrix factorization (SNMF) within feedback control systems, called the SVD System and SNMF System, to control numerous subsystems with a reduced number of control inputs. The subsystems are coupled using a row-column structure to allow mn subsystems to be controlled using m+n inputs. Past techniques for controlling systems in this row-column structure have focused on scheduling procedures that offer limited performance. The SVD and SNMF Systems permit simultaneous control of every subsystem, which increases the convergence rate by an order of magnitude compared with previous methods. In addition to closed loop control, open loop procedures using the SVD and SNMF are compared with previous scheduling procedures, demonstrating significant performance improvements. This thesis presents theoretical results for the controllability of systems using the row-column structure and for the stability and performance of the SVD and SNMF Systems. Practical challenges to the implementation of the SVD and SNMF Systems are also examined. Numerous simulation examples are provided, in particular, a dynamic simulation of a pin array device, called Digital Clay, and two physical demonstrations are used to assess the feasibility of the SVD and SNMF Systems for specific applications.
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Goda, Sai Bharath. "Recommender system for recipes." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/17741.

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Master of Science
Department of Computing and Information Sciences
Daniel A. Anderson
Most of the e-commerce websites like Amazon, EBay, hotels, trip advisor etc. use recommender systems to recommend products to their users. Some of them use the knowledge of history/ of all users to recommend what kind of products the current user may like (Collaborative filtering) and some use the knowledge of the products which the user is interested in and make recommendations (Content based filtering). An example is Amazon which uses both kinds of techniques.. These recommendation systems can be represented in the form of a graph where the nodes are users and products and edges are between users and products. The aim of this project is to build a recommender system for recipes by using the data from allrecipes.com. Allrecipes.com is a popular website used all throughout the world to post recipes, review them and rate them. To understand the data set one needs to know how the recipes are posted and rated in allrecipes.com, whose details are given in the paper. The network of allrecipes.com consists of users, recipes and ingredients. The aim of this research project is to extensively study about two algorithms adsorption and matrix factorization, which are evaluated on homogeneous networks and try them on the heterogeneous networks and analyze their results. This project also studies another algorithm that is used to propagate influence from one network to another network. To learn from one network and propagate the same information to another network we compute flow (influence of one network on another) as described in [7]. The paper introduces a variant of adsorption that takes the flow values into account and tries to make recommendations in the user-recipe and the user-ingredient networks. The results of this variant are analyzed in depth in this paper.
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Hedlund, Jesper, and Tengstrand Emma Nilsson. "A Comparison between Different Recommender System Approaches for a Book and an Author Recommender System." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166378.

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A recommender system is a popular tool used by companies to increase customer satisfaction and to increase revenue. Collaborative filtering and content-based filtering are the two most common approaches when implementing a recommender system, where the former provides recommendations based on user behaviour, and the latter uses the characteristics of the items that are recommended. The aim of the study was to develop and compare different recommender system approaches, for both book and author recommendations and their ability to predict user ratings of an e-book application. The evaluation of the models was done by measuring root mean square error (RMSE) and mean absolute error (MAE). Two pure models were developed, one based on collaborative filtering and one based on content-based filtering. Also, three different hybrid models using a combination of the two pure approaches were developed and compared to the pure models. The study also explored how aggregation of book data to author level could be used to implement an author recommender system. The results showed that the aggregated author data was more difficult to predict. However, it was difficult to draw any conclusions of the performance on author data due to the data aggregation. Although it was clear that it was possible to derive author recommendations based on data from books. The study also showed that the collaborative filtering model performed better than the content-based filtering model according to RMSE but not according to MAE. The lowest RMSE and MAE, however, were achieved by combining the two approaches in a hybrid model.
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Broman, Nils. "Comparasion of recommender systems for stock inspiration." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176408.

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Recommender systems are apparent in our lives through multiple different ways, such asrecommending what items to purchase when online shopping, recommending movies towatch and recommending restaurants in your area. This thesis aims to apply the sametechniques of recommender systems on a new area, namely stock recommendations basedon your current portfolio. The data used was collected from a social media platform forinvestments, Shareville, and contained multiple users portfolios. The implicit data wasthen used to train matrix factorization models, and the state-of-the-art LightGCN model.Experiments regarding different data splits was also conducted. Results indicate that rec-ommender systems techniques can be applied successfully to generate stock recommen-dations. Also, that the relative performance of the models on this dataset are in line withprevious research. LightGCN greatly outperforms matrix factorization models on this pro-posed dataset. The results also show that different data splits also greatly impact the re-sults, which is discussed in further detail in this thesis.
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Sundaramurthy, Roshni. "Recommender System for Gym Customers." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166147.

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Recommender systems provide new opportunities for retrieving personalized information on the Internet. Due to the availability of big data, the fitness industries are now focusing on building an efficient recommender system for their end-users. This thesis investigates the possibilities of building an efficient recommender system for gym users. BRP Systems AB has provided the gym data for evaluation and it consists of approximately 896,000 customer interactions with 8 features. Four different matrix factorization methods, Latent semantic analysis using Singular value decomposition, Alternating least square, Bayesian personalized ranking, and Logistic matrix factorization that are based on implicit feedback are applied for the given data. These methods decompose the implicit data matrix of user-gym group activity interactions into the product of two lower-dimensional matrices. They are used to calculate the similarities between the user and activity interactions and based on the score, the top-k recommendations are provided. These methods are evaluated by the ranking metrics such as Precision@k, Mean average precision (MAP) @k, Area under the curve (AUC) score, and Normalized discounted cumulative gain (NDCG) @k. The qualitative analysis is also performed to evaluate the results of the recommendations. For this specific dataset, it is found that the optimal method is the Alternating least square method which achieved around 90\% AUC for the overall system and managed to give personalized recommendations to the users.
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Johansson, Angela. "Distributed System for Factorisation of Large Numbers." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1883.

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This thesis aims at implementing methods for factorisation of large numbers. Seeing that there is no deterministic algorithm for finding the prime factors of a given number, the task proves rather difficult. Luckily, there have been developed some effective probabilistic methods since the invention of the computer so that it is now possible to factor numbers having about 200 decimal digits. This however consumes a large amount of resources and therefore, virtually all new factorisations are achieved using the combined power of many computers in a distributed system.

The nature of the distributed system can vary. The original goal of the thesis was to develop a client/server system that allows clients to carry out a portion of the overall computations and submit the result to the server.

Methods for factorisation discussed for implementation in the thesis are: the quadratic sieve, the number field sieve and the elliptic curve method. Actually implemented was only a variant of the quadratic sieve: the multiple polynomial quadratic sieve (MPQS).

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Nguyen, Le Ha Vy. "Stability and stabilization of several classes of fractional systems with delays." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112387/document.

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Nous considérons deux classes de systèmes fractionnaires linéaires invariants dans le temps avec des ordres commensurables et des retards discrets. La première est composée de systèmes fractionnaires à entrées multiples et à une sortie avec des retards en entrées ou en sortie. La seconde se compose de systèmes fractionnaires de type neutre avec retards commensurables. Nous étudions la stabilisation de la première classe de systèmes à l'aide de l'approche de factorisation. Nous obtenons des factorisations copremières à gauche et à droite et les facteurs de Bézout associés: ils permettent de constituer l'ensemble des contrôleurs stabilisants. Pour la deuxième classe de systèmes, nous nous sommes intéressés au cas critique où certaines chaînes de pôles sont asymptotiques à l'axe imaginaire. Tout d'abord, nous réalisons une approximation des pôles asymptotiques afin de déterminer leur emplacement par rapport à l'axe. Le cas échéant, des conditions nécessaires et suffisantes de stabilité H-infini sont données. Cette analyse de stabilité est ensuite étendue aux systèmes à retard classiques ayant la même forme. Enfin, nous proposons une approche unifiée pour les deux classes de systèmes à retards commensurables de type neutre (standards et fractionnaires). Ensuite, la stabilisation d'une sous-classe de systèmes neutres fractionnaires est étudiée. Premièrement, l'ensemble de tous les contrôleurs stabilisants est obtenu. Deuxièmement, nous prouvons que pour une grande classe de contrôleurs fractionnaires à retards il est impossible d'éliminer dans la boucle fermée les chaînes de pôles asymptotiques à l'axe imaginaire si de telles chaînes sont présentes dans les systèmes à contrôler
We consider two classes of linear time-invariant fractional systems with commensurate orders and discrete delays. The first one consists of multi-input single-output fractional systems with output or input delays. The second one consists of single-input single-output fractional neutral systems with commensurate delays. We study the stabilization of the first class of systems using the factorization approach. We derive left and right coprime factorizations and Bézout factors, which are the elements to constitute the set of all stabilizing controllers. For the second class of systems, we are interested in the critical case where some chains of poles are asymptotic to the imaginary axis. First, we approximate asymptotic poles in order to determine their location relative to the axis. Then, when appropriate, necessary and sufficient conditions for H-infinity-stability are derived. This stability analysis is then extended to classical delay systems of the same form and finally a unified approach for both classes of neutral delay systems with commensurate delays (standard and fractional) is proposed. Next, the stabilization of a subclass of fractional neutral systems is studied. First, the set of all stabilizing controllers is derived. Second, we prove that a large class of fractional controllers with delays cannot eliminate in the closed loop chains of poles asymptotic to the imaginary axis if such chains are present in the controlled systems
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Kišac, Matej. "Distribuované aplikace s využitím frameworku Windows Communication Foundation." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242060.

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This thesis deals with distributed applications and WCF framework. The first part is based on theoretical information about distributed systems and we also concentrate on models of distributed systems. Next part describes WCF framework and key elements of WCF application. The following chapter is designated to introduce information about prime factorization. Then the knowledge from previous parts is used to create examples of service-oriented applications. In conclusion we discuss main parts of designing distributed application to solve factorization problem. Finally the comparison of distributed and dedicated application is made.
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Books on the topic "Factorization system"

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Vidyasagar, M. Control system synthesis: A factorization approach. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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Control system synthesis: A factorization approach. Cambridge, Mass: MIT Press, 1985.

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Gohberg, Israel, Nenad Manojlovic, and António Ferreira dos Santos, eds. Factorization and Integrable Systems. Basel: Birkhäuser Basel, 2003. http://dx.doi.org/10.1007/978-3-0348-8003-9.

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Naik, Vijay K. Data traffic reduction schemes for Cholesky factorization on asynchronous multiprocessor systems. Hampton, Va: ICASE, 1989.

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Symeonidis, Panagiotis, and Andreas Zioupos. Matrix and Tensor Factorization Techniques for Recommender Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41357-0.

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Jonathan, Wu Q. M., ed. Guide to three dimensional structure and motion factorization. London: Springer, 2011.

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Rothberg, Edward. Improved load distribution in parallel sparse Cholesky factorization. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1994.

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1928-, Gohberg I., Manojlovic Nenad 1962-, and Santos, António Ferreira dos, 1939-, eds. Factorization and integrable systems: Summer school in Faro, Portugal, September 2000. Boston: Birkhäuser, 2003.

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Gohberg, Israel. Factorization and Integrable Systems: Summer School in Faro, Portugal, September 2000. Basel: Birkhäuser Basel, 2003.

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Burns, John A. Factorization and reduction methods for optimal control of distributed parameter systems. Hampton, Va: ICASE, 1985.

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Book chapters on the topic "Factorization system"

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Bressoud, David M. "The RSA Public Key Crypto-System." In Factorization and Primality Testing, 43–57. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-4544-5_4.

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Lekshmi Priya, T., and Harikumar Sandhya. "Matrix Factorization for Recommendation System." In Advances in Intelligent Systems and Computing, 267–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3514-7_22.

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Wu, Mu-En, Raylin Tso, and Hung-Min Sun. "On the Improvement of Fermat Factorization." In Network and System Security, 380–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34601-9_29.

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Wang, Yanghao, Hailong Sun, and Richong Zhang. "AdaMF:Adaptive Boosting Matrix Factorization for Recommender System." In Web-Age Information Management, 43–54. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08010-9_7.

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Zhang, Ronghua, Zhenlong Zhu, Changzheng Liu, Yuhua Li, and Ruixuan Li. "Deep Neural Factorization Machine for Recommender System." In Knowledge Science, Engineering and Management, 273–86. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10986-7_22.

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Dong, Shi-Hai. "CONTROLLABILITY OF QUANTUM SYSTEM FOR THE PT-LIKE POTENTIAL WITH DYNAMIC GROUP SU(1, 1)." In Factorization Method in Quantum Mechanics, 229–34. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5796-0_20.

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Kunaver, Matevž, and Iztok Fajfar. "Grammatical Evolution in a Matrix Factorization Recommender System." In Artificial Intelligence and Soft Computing, 392–400. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39378-0_34.

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Zhou, Juming, Dong Wang, Yue Ding, and Litian Yin. "SocialFM: A Social Recommender System with Factorization Machines." In Web-Age Information Management, 286–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39937-9_22.

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Alpay, D., A. Dijksma, J. Rovnyak, and H. S. V. de Snoo. "Realization and Factorization in Reproducing Kernel Pontryagin Spaces." In Operator Theory, System Theory and Related Topics, 43–65. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8247-7_3.

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Li, Fangfang, Guandong Xu, Longbing Cao, Xiaozhong Fan, and Zhendong Niu. "CGMF: Coupled Group-Based Matrix Factorization for Recommender System." In Lecture Notes in Computer Science, 189–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41230-1_16.

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Conference papers on the topic "Factorization system"

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Hopf, F. A., and C. M. Bowden. "Heuristic Model for Fluctuations in Mirrorless Optical Bistability." In Optical Bistability. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/obi.1985.we7.

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Recent interest has focussed on the subject of mirrorless optical bistability, the simplest system showing bistability theoretically is a system of N ≥ 2 atoms. Microscopic theory is derived from quantum theory by a factorization of binary products of operators. There are strong inter atomic interactions in the bistable regime, it is unlikely that these factorizations are valid. Quantum calculations that avoid factorization imply that bistability does not exist. Hence in the previous bistability conference there was skepticism that bistability could really occur in a mirrorless system with only a few atoms.
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Badawy, Mohammad Osama, Yasser Y. Hanafy, and Ramy Eltarras. "LU factorization using multithreaded system." In 2012 22nd International Conference on Computer Theory and Applications (ICCTA). IEEE, 2012. http://dx.doi.org/10.1109/iccta.2012.6523540.

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Jayathilaka, Dineth Keshawa, Gayumi Nimesha Kottage, Kapuliyanage Chasika Chankuma, Gamage Upeksha Ganegoda, and Thanuja Sandanayake. "Hybrid Weight Factorization Recommendation System." In 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2018. http://dx.doi.org/10.1109/icter.2018.8615467.

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Zhu, Jianhao, Wenming Ma, and Yulong Song. "Attentive Matrix Factorization for Recommender System." In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020. http://dx.doi.org/10.1109/cisp-bmei51763.2020.9263558.

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Yang, Wei Feng, Min Wang, and Zhou Chen. "Fast Probabilistic Matrix Factorization for recommender system." In 2014 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2014. http://dx.doi.org/10.1109/icma.2014.6885990.

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Reshak, Kaiser A., Ban N. Dhannoon, and Zainab N. Sultani. "Hybrid recommender system based on matrix factorization." In THE SECOND INTERNATIONAL SCIENTIFIC CONFERENCE (SISC2021): College of Science, Al-Nahrain University. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0118335.

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Niu, Shaohua, and D. Grant Fisher. "MIMO System Identification using Augmented UD Factorization." In 1991 American Control Conference. IEEE, 1991. http://dx.doi.org/10.23919/acc.1991.4791462.

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Zhou, Bowen, and Raymond Wong. "Effective Matrix Factorization for Online Rating Prediction." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2017. http://dx.doi.org/10.24251/hicss.2017.144.

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Santos, Ricardo, Renan Marks, Rafael Alves, Felipe Araujo, and Renato Santos. "Instruction decoders based on pattern factorization." In 2015 28th IEEE International System-on-Chip Conference (SOCC). IEEE, 2015. http://dx.doi.org/10.1109/socc.2015.7406936.

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Simsekli, U., T. Birdal, E. Koc, and A. T. Cemgil. "A factorization based recommender system for online services." In 2013 21st Signal Processing and Communications Applications Conference (SIU). IEEE, 2013. http://dx.doi.org/10.1109/siu.2013.6531312.

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Reports on the topic "Factorization system"

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Kurzak, Jakub, Pitior Luszczek, Mathieu Faverge, and Jack Dongarra. LU Factorization with Partial Pivoting for a Multi-CPU, Multi-GPU Shared Memory System. Office of Scientific and Technical Information (OSTI), March 2012. http://dx.doi.org/10.2172/1173291.

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