The document describes the development of a mobile game to help students learn Boolean logic and the Quine-McCluskey algorithm. The game allows users to minimize Boolean expressions by solving Karnaugh maps of varying difficulty. The authors implemented the Quine-McCluskey algorithm in Swift to generate optimal solutions and check user answers. They discuss challenges like the algorithm's exponential time complexity and cases with no essential prime implicants. The prototype lets users set the problem size and difficulty to generate random Karnaugh maps to solve.
Mining at scale with latent factor models for matrix completionFabio Petroni, PhD
Â
PhD Thesis
F. Petroni:
"Mining at scale with latent factor models for matrix completion."
Sapienza University of Rome, 2016.
Abstract: "Predicting which relationships are likely to occur between real-world objects is a key task for several applications. For instance, recommender systems aim at predicting the existence of unknown relationships between users and items, and exploit this information to provide personalized suggestions for items to be of use to a specific user. Matrix completion techniques aim at solving this task, identifying and leveraging the latent factors that triggered the the creation of known relationships to infer missing ones.
This problem, however, is made challenging by the size of todayâs datasets. One way to handle such large-scale data, in a reasonable amount of time, is to distribute the matrix completion procedure over a cluster of commodity machines. However, current approaches lack of efficiency and scalability, since, for instance, they do not minimize the communication or ensure a balance workload in the cluster.
A further aspect of matrix completion techniques we investigate is how to improve their prediction performance. This can be done, for instance, considering the context in which relationships have been captured. However, incorporating generic contextual information within a matrix completion algorithm is a challenging task.
In the first part of this thesis, we study distributed matrix completion solutions, and address the above issues by examining input slicing techniques based on graph partitioning algorithms. In the second part of the thesis, we focus on context-aware matrix completion techniques, providing solutions that can work both (i) when the revealed entries in the matrix have multiple values and (ii) all the same value."
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Â
Optimal components assignment problem subject to system reliability, total lead-time, and total cost constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the presented problem. The optimal solution found by the proposed approach is characterized by maximum reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different examples taken from the literature to illustrate its efficiency in comparison with other previous methods.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Â
Optimal components assignment problem subject to system reliability, total lead-time, and total cost
constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy
membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the
presented problem. The optimal solution found by the proposed approach is characterized by maximum
reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different
examples taken from the literature to illustrate its efficiency in comparison with other previous methods
F. Petroni, L. Querzoni, R. Beraldi, M. Paolucci:
"LCBM: Statistics-Based Parallel Collaborative Filtering."
In: Proceedings of the 17th International Conference on Business Information Systems (BIS), 2014.
Abstract: "In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet industry. Collaborative Filtering (CF) represents todayâs a widely adopted strategy to build recommendation engines. The most advanced CF techniques (i.e. those based on matrix factorization) provide high quality results, but may incur prohibitive computational costs when applied to very large data sets. In this paper we present Linear Classifier of Beta distributions Means (LCBM), a novel collaborative filtering algorithm for binary ratings that is (i) inherently parallelizable and (ii) provides results whose quality is on-par with state-of-the-art solutions (iii) at a fraction of the computational cost."
F. Petroni and L. Querzoni:
"GASGD: Stochastic Gradient Descent for Distributed Asynchronous Matrix Completion via Graph Partitioning."
In: Proceedings of the 8th ACM Conference on Recommender Systems (RecSys), 2014.
Abstract: "Matrix completion latent factors models are known to be an effective method to build recommender systems. Currently,
stochastic gradient descent (SGD) is considered one of the best latent factor-based algorithm for matrix completion. In this paper we discuss GASGD, a distributed asynchronous variant of SGD for large-scale matrix completion, that (i) leverages data partitioning schemes based on graph partitioning techniques, (ii) exploits specific characteristics of the input data and (iii) introduces an explicit parameter to tune synchronization frequency among the computing nodes. We empirically show how, thanks to these features, GASGD achieves a fast convergence rate incurring in smaller communication cost with respect to current asynchronous distributed SGD implementations."
Mining at scale with latent factor models for matrix completionFabio Petroni, PhD
Â
PhD Thesis
F. Petroni:
"Mining at scale with latent factor models for matrix completion."
Sapienza University of Rome, 2016.
Abstract: "Predicting which relationships are likely to occur between real-world objects is a key task for several applications. For instance, recommender systems aim at predicting the existence of unknown relationships between users and items, and exploit this information to provide personalized suggestions for items to be of use to a specific user. Matrix completion techniques aim at solving this task, identifying and leveraging the latent factors that triggered the the creation of known relationships to infer missing ones.
This problem, however, is made challenging by the size of todayâs datasets. One way to handle such large-scale data, in a reasonable amount of time, is to distribute the matrix completion procedure over a cluster of commodity machines. However, current approaches lack of efficiency and scalability, since, for instance, they do not minimize the communication or ensure a balance workload in the cluster.
A further aspect of matrix completion techniques we investigate is how to improve their prediction performance. This can be done, for instance, considering the context in which relationships have been captured. However, incorporating generic contextual information within a matrix completion algorithm is a challenging task.
In the first part of this thesis, we study distributed matrix completion solutions, and address the above issues by examining input slicing techniques based on graph partitioning algorithms. In the second part of the thesis, we focus on context-aware matrix completion techniques, providing solutions that can work both (i) when the revealed entries in the matrix have multiple values and (ii) all the same value."
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Â
Optimal components assignment problem subject to system reliability, total lead-time, and total cost constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the presented problem. The optimal solution found by the proposed approach is characterized by maximum reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different examples taken from the literature to illustrate its efficiency in comparison with other previous methods.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Â
Optimal components assignment problem subject to system reliability, total lead-time, and total cost
constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy
membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the
presented problem. The optimal solution found by the proposed approach is characterized by maximum
reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different
examples taken from the literature to illustrate its efficiency in comparison with other previous methods
F. Petroni, L. Querzoni, R. Beraldi, M. Paolucci:
"LCBM: Statistics-Based Parallel Collaborative Filtering."
In: Proceedings of the 17th International Conference on Business Information Systems (BIS), 2014.
Abstract: "In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet industry. Collaborative Filtering (CF) represents todayâs a widely adopted strategy to build recommendation engines. The most advanced CF techniques (i.e. those based on matrix factorization) provide high quality results, but may incur prohibitive computational costs when applied to very large data sets. In this paper we present Linear Classifier of Beta distributions Means (LCBM), a novel collaborative filtering algorithm for binary ratings that is (i) inherently parallelizable and (ii) provides results whose quality is on-par with state-of-the-art solutions (iii) at a fraction of the computational cost."
F. Petroni and L. Querzoni:
"GASGD: Stochastic Gradient Descent for Distributed Asynchronous Matrix Completion via Graph Partitioning."
In: Proceedings of the 8th ACM Conference on Recommender Systems (RecSys), 2014.
Abstract: "Matrix completion latent factors models are known to be an effective method to build recommender systems. Currently,
stochastic gradient descent (SGD) is considered one of the best latent factor-based algorithm for matrix completion. In this paper we discuss GASGD, a distributed asynchronous variant of SGD for large-scale matrix completion, that (i) leverages data partitioning schemes based on graph partitioning techniques, (ii) exploits specific characteristics of the input data and (iii) introduces an explicit parameter to tune synchronization frequency among the computing nodes. We empirically show how, thanks to these features, GASGD achieves a fast convergence rate incurring in smaller communication cost with respect to current asynchronous distributed SGD implementations."
Event Coreference Resolution using Mincut based Graph Clustering cscpconf
Â
To extract participants of an event instance, it is necessary to identify all the sentences that
describe the event instance. The set of all sentences referring to the same event instance are said
to be corefering each other. Our proposed approach formulates the event coreference resolution
as a graph based clustering model. It identifies the corefering sentences using minimum cut
(mincut) based on similarity score between each pair of sentences at various levels such as
trigger word similarity, time stamp similarity, entity similarity and semantic similarity. It
achieves good B-Cubed F-measure score with some loss in recall.
A wide variety of combinatorial problems can be viewed as Weighted Constraint Satisfaction Problems (WCSPs). All resolution methods have an exponential time complexity for big instances. Moreover, they combine several techniques, use a wide variety of concepts and notations that are difficult to understand and implement. In this paper, we model this problem in terms of an original 0-1 quadratic programming subject to linear constraints. This model is validated by the proposed and demonstrated theorem. View its performance, we use the Hopfield neural network to solve the obtained model basing on original energy function. To validate our model, we solve several instances of benchmarking WCSP. Our approach has the same memory complexity as the HNN and the same time complexity as Euler-Cauchy method. In this regard, our approach recognizes the optimal solution of the said instances.
MIXED 0â1 GOAL PROGRAMMING APPROACH TO INTERVAL-VALUED BILEVEL PROGRAMMING PR...cscpconf
Â
This paper presents how the mixed 0-1 programming in the framework of goal programming (GP) can be used to solve interval-valued fractional bilevel programming (IVFBLP) problems by employing genetic algorithm (GA) in a hierarchical decision making system. In the model formulation of the problem, a goal achievement function for minimizing the lower-bounds of the necessary regret intervals defined for the target intervals of achieving the goals and thereby arriving at a compromise decision is constructed by using both the aspects of âminsumâ and âminmaxâ approaches in GP. In the decision process, an GA scheme is employed for execution
of the problems at the two stages, target interval specification and optimal decision determination, for distribution of decision powers to the decision makers (DMs) in the order of hierarchy. A numerical example is provided to illustrate the potential use of the approach.
A SECURE DIGITAL SIGNATURE SCHEME WITH FAULT TOLERANCE BASED ON THE IMPROVED ...csandit
Â
Fault tolerance and data security are two important issues in modern communication systems.
In this paper, we propose a secure and efficient digital signature scheme with fault tolerance
based on the improved RSA system. The proposed scheme for the RSA cryptosystem contains
three prime numbers and overcome several attacks possible on RSA. By using the Chinese
Reminder Theorem (CRT) the proposed scheme has a speed improvement on the RSA decryption
side and it provides high security also.
Penalty Function Method For Solving Fuzzy Nonlinear Programming Problempaperpublications3
Â
Abstract: In this work, the fuzzy nonlinear programming problem (FNLPP) has been developed and their result have also discussed. The numerical solutions of crisp problems and have been compared and the fuzzy solution and its effectiveness have also been presented and discussed. The penalty function method has been developed and mixed with Nelder and Mendâs algorithm of direct optimization problem solutionhave been used together to solve this FNLPP.
Keyword:Fuzzy set theory, fuzzy numbers, decision making, nonlinear programming, Nelder and Mendâs algorithm, penalty function method.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Interactive Fuzzy Goal Programming approach for Tri-Level Linear Programming ...IJERA Editor
Â
The aim of this paper is to present an interactive fuzzy goal programming approach to determine the preferred
compromise solution to Tri-level linear programming problems considering the imprecise nature of the decision
makersâ judgments for the objectives. Using the concept of goal programming, fuzzy set theory, in combination
with interactive programming, and improving the membership functions by means of changing the tolerances of
the objectives provide a satisfactory compromise (near to ideal) solution to the upper level decision makers.
Two numerical examples for three-level linear programming problems have been solved to demonstrate the
feasibility of the proposed approach. The performance of the proposed approach was evaluated by using of
metric distance functions with other approaches.
Design of optimized Interval Arithmetic MultiplierVLSICS Design
Â
Many DSP and Control applications that require the user to know how various numerical errors(uncertainty) affect the result. This uncertainty is eliminated by replacing non-interval values with intervals. Since most DSPs operate in real time environments, fast processors are required to implement interval arithmetic. The goal is to develop a platform in which Interval Arithmetic operations are performed at the same computational speed as present day signal processors. So we have proposed the design and implementation of Interval Arithmetic multiplier, which operates with IEEE 754 numbers. The proposed unit consists of a floating point CSD multiplier, Interval operation selector. This architecture implements an algorithm which is faster than conventional algorithm of Interval multiplier . The cost overhead of the proposed unit is 30% with respect to a conventional floating point multiplier. The
performance of proposed architecture is better than that of a conventional CSD floating-point multiplier, as it can perform both interval multiplication and floating-point multiplication as well as Interval comparisons
A NEW ALGORITHM FOR SOLVING FULLY FUZZY BI-LEVEL QUADRATIC PROGRAMMING PROBLEMSorajjournal
Â
This paper is concerned with new method to find the fuzzy optimal solution of fully fuzzy bi-level non-linear (quadratic) programming (FFBLQP) problems where all the coefficients and decision variables of both objective functions and the constraints are triangular fuzzy numbers (TFNs). A new method is based on decomposed the given problem into bi-level problem with three crisp quadratic objective functions and bounded variables constraints. In order to often a fuzzy optimal solution of the FFBLQP problems, the concept of tolerance membership function is used to develop a fuzzy max-min decision model for generating satisfactory fuzzy solution for FFBLQP problems in which the upper-level decision maker (ULDM) specifies his/her objective functions and decisions with possible tolerances which are described by membership functions of fuzzy set theory. Then, the lower-level decision maker (LLDM) uses this preference information for ULDM and solves his/her problem subject to the ULDMs restrictions. Finally, the decomposed method is illustrated by numerical example.
A new RSA public key encryption scheme with chaotic maps IJECEIAES
Â
Public key cryptography has received great attention in the field of information exchange through insecure channels. In this paper, we combine the Dependent-RSA (DRSA) and chaotic maps (CM) to get a new secure cryptosystem, which depends on both integer factorization and chaotic maps discrete logarithm (CMDL). Using this new system, the scammer has to go through two levels of reverse engineering, concurrently, so as to perform the recovery of original text from the cipher-text has been received. Thus, this new system is supposed to be more sophisticated and more secure than other systems. We prove that our new cryptosystem does not increase the overhead in performing the encryption process or the decryption process considering that it requires minimum operations in both. We show that this new cryptosystem is more efficient in terms of performance compared with other encryption systems, which makes it more suitable for nodes with limited computational ability.
Event Coreference Resolution using Mincut based Graph Clustering cscpconf
Â
To extract participants of an event instance, it is necessary to identify all the sentences that
describe the event instance. The set of all sentences referring to the same event instance are said
to be corefering each other. Our proposed approach formulates the event coreference resolution
as a graph based clustering model. It identifies the corefering sentences using minimum cut
(mincut) based on similarity score between each pair of sentences at various levels such as
trigger word similarity, time stamp similarity, entity similarity and semantic similarity. It
achieves good B-Cubed F-measure score with some loss in recall.
A wide variety of combinatorial problems can be viewed as Weighted Constraint Satisfaction Problems (WCSPs). All resolution methods have an exponential time complexity for big instances. Moreover, they combine several techniques, use a wide variety of concepts and notations that are difficult to understand and implement. In this paper, we model this problem in terms of an original 0-1 quadratic programming subject to linear constraints. This model is validated by the proposed and demonstrated theorem. View its performance, we use the Hopfield neural network to solve the obtained model basing on original energy function. To validate our model, we solve several instances of benchmarking WCSP. Our approach has the same memory complexity as the HNN and the same time complexity as Euler-Cauchy method. In this regard, our approach recognizes the optimal solution of the said instances.
MIXED 0â1 GOAL PROGRAMMING APPROACH TO INTERVAL-VALUED BILEVEL PROGRAMMING PR...cscpconf
Â
This paper presents how the mixed 0-1 programming in the framework of goal programming (GP) can be used to solve interval-valued fractional bilevel programming (IVFBLP) problems by employing genetic algorithm (GA) in a hierarchical decision making system. In the model formulation of the problem, a goal achievement function for minimizing the lower-bounds of the necessary regret intervals defined for the target intervals of achieving the goals and thereby arriving at a compromise decision is constructed by using both the aspects of âminsumâ and âminmaxâ approaches in GP. In the decision process, an GA scheme is employed for execution
of the problems at the two stages, target interval specification and optimal decision determination, for distribution of decision powers to the decision makers (DMs) in the order of hierarchy. A numerical example is provided to illustrate the potential use of the approach.
A SECURE DIGITAL SIGNATURE SCHEME WITH FAULT TOLERANCE BASED ON THE IMPROVED ...csandit
Â
Fault tolerance and data security are two important issues in modern communication systems.
In this paper, we propose a secure and efficient digital signature scheme with fault tolerance
based on the improved RSA system. The proposed scheme for the RSA cryptosystem contains
three prime numbers and overcome several attacks possible on RSA. By using the Chinese
Reminder Theorem (CRT) the proposed scheme has a speed improvement on the RSA decryption
side and it provides high security also.
Penalty Function Method For Solving Fuzzy Nonlinear Programming Problempaperpublications3
Â
Abstract: In this work, the fuzzy nonlinear programming problem (FNLPP) has been developed and their result have also discussed. The numerical solutions of crisp problems and have been compared and the fuzzy solution and its effectiveness have also been presented and discussed. The penalty function method has been developed and mixed with Nelder and Mendâs algorithm of direct optimization problem solutionhave been used together to solve this FNLPP.
Keyword:Fuzzy set theory, fuzzy numbers, decision making, nonlinear programming, Nelder and Mendâs algorithm, penalty function method.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Interactive Fuzzy Goal Programming approach for Tri-Level Linear Programming ...IJERA Editor
Â
The aim of this paper is to present an interactive fuzzy goal programming approach to determine the preferred
compromise solution to Tri-level linear programming problems considering the imprecise nature of the decision
makersâ judgments for the objectives. Using the concept of goal programming, fuzzy set theory, in combination
with interactive programming, and improving the membership functions by means of changing the tolerances of
the objectives provide a satisfactory compromise (near to ideal) solution to the upper level decision makers.
Two numerical examples for three-level linear programming problems have been solved to demonstrate the
feasibility of the proposed approach. The performance of the proposed approach was evaluated by using of
metric distance functions with other approaches.
Design of optimized Interval Arithmetic MultiplierVLSICS Design
Â
Many DSP and Control applications that require the user to know how various numerical errors(uncertainty) affect the result. This uncertainty is eliminated by replacing non-interval values with intervals. Since most DSPs operate in real time environments, fast processors are required to implement interval arithmetic. The goal is to develop a platform in which Interval Arithmetic operations are performed at the same computational speed as present day signal processors. So we have proposed the design and implementation of Interval Arithmetic multiplier, which operates with IEEE 754 numbers. The proposed unit consists of a floating point CSD multiplier, Interval operation selector. This architecture implements an algorithm which is faster than conventional algorithm of Interval multiplier . The cost overhead of the proposed unit is 30% with respect to a conventional floating point multiplier. The
performance of proposed architecture is better than that of a conventional CSD floating-point multiplier, as it can perform both interval multiplication and floating-point multiplication as well as Interval comparisons
A NEW ALGORITHM FOR SOLVING FULLY FUZZY BI-LEVEL QUADRATIC PROGRAMMING PROBLEMSorajjournal
Â
This paper is concerned with new method to find the fuzzy optimal solution of fully fuzzy bi-level non-linear (quadratic) programming (FFBLQP) problems where all the coefficients and decision variables of both objective functions and the constraints are triangular fuzzy numbers (TFNs). A new method is based on decomposed the given problem into bi-level problem with three crisp quadratic objective functions and bounded variables constraints. In order to often a fuzzy optimal solution of the FFBLQP problems, the concept of tolerance membership function is used to develop a fuzzy max-min decision model for generating satisfactory fuzzy solution for FFBLQP problems in which the upper-level decision maker (ULDM) specifies his/her objective functions and decisions with possible tolerances which are described by membership functions of fuzzy set theory. Then, the lower-level decision maker (LLDM) uses this preference information for ULDM and solves his/her problem subject to the ULDMs restrictions. Finally, the decomposed method is illustrated by numerical example.
A new RSA public key encryption scheme with chaotic maps IJECEIAES
Â
Public key cryptography has received great attention in the field of information exchange through insecure channels. In this paper, we combine the Dependent-RSA (DRSA) and chaotic maps (CM) to get a new secure cryptosystem, which depends on both integer factorization and chaotic maps discrete logarithm (CMDL). Using this new system, the scammer has to go through two levels of reverse engineering, concurrently, so as to perform the recovery of original text from the cipher-text has been received. Thus, this new system is supposed to be more sophisticated and more secure than other systems. We prove that our new cryptosystem does not increase the overhead in performing the encryption process or the decryption process considering that it requires minimum operations in both. We show that this new cryptosystem is more efficient in terms of performance compared with other encryption systems, which makes it more suitable for nodes with limited computational ability.
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Grasp approach to rcpsp with min max robustness objectivecsandit
Â
This paper deals with the Resource-Constrained Project scheduling Problem (RCPSP) under
activity duration uncertainty. Based on scenarios, the object is to minimize the worst-case
performance among a set of initial scenarios which is referred to as the min-max robustness
objective. Due to the complexity of the tackled problem, we propose the application of the
GRASP method which is qualified as a simple and effective multi-start metaheuristic. The
proposed approach incorporates an adaptive greedy function based on priority rules to
construct new solutions, and a local search with a forward-backward heuristic in the
improvement phase. Two different benchmark data sets are investigated, the Patterson set and
the PSPLIB J30 set. Comparative results show that the proposed enhanced GRASP outperforms
the basic procedure in robustness optimization.
FAST ALGORITHMS FOR UNSUPERVISED LEARNING IN LARGE DATA SETScsandit
Â
The ability to mine and extract useful information automatically, from large datasets, is a
common concern for organizations (having large datasets), over the last few decades. Over the
internet, data is vastly increasing gradually and consequently the capacity to collect and store
very large data is significantly increasing.
Existing clustering algorithms are not always efficient and accurate in solving clustering
problems for large datasets.
However, the development of accurate and fast data classification algorithms for very large
scale datasets is still a challenge. In this paper, various algorithms and techniques especially,
approach using non-smooth optimization formulation of the clustering problem, are proposed
for solving the minimum sum-of-squares clustering problems in very large datasets. This
research also develops accurate and real time L2-DC algorithm based with the incremental
approach to solve the minimum
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Â
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
For some management programming problems, multiple objectives to be optimized rather than a single objective, and objectives can be expressed with ratio equations such as return/investment, operating profit/net-sales, profit/manufacturing cost, etc. In this paper, we proposed the transformation characteristics to solve the multi objective linear fractional programming (MOLFP) problems. If a MOLFP problem with both the numerators and the denominators of the objectives are linear functions and some technical linear restrictions are satisfied, then it is defined as a multi objective linear fractional programming problem MOLFPP in this research. The transformation characteristics are illustrated and the solution procedure and numerical example are presented.
APPLYING TRANSFORMATION CHARACTERISTICS TO SOLVE THE MULTI OBJECTIVE LINEAR F...ijcsit
Â
For some management programming problems, multiple objectives to be optimized rather than a single objective, and objectives can be expressed with ratio equations such as return/investment, operating
profit/net-sales, profit/manufacturing cost, etc. In this paper, we proposed the transformation characteristics to solve the multi objective linear fractional programming (MOLFP) problems. If a MOLFP problem with both the numerators and the denominators of the objectives are linear functions and some
technical linear restrictions are satisfied, then it is defined as a multi objective linear fractional programming problem MOLFPP in this research. The transformation characteristics are illustrated and the solution procedure and numerical example are presented.
For some management programming problems, multiple objectives to be optimized rather than a single
objective, and objectives can be expressed with ratio equations such as return/investment, operating
profit/net-sales, profit/manufacturing cost, etc. In this paper, we proposed the transformation
characteristics to solve the multi objective linear fractional programming (MOLFP) problems. If a MOLFP
problem with both the numerators and the denominators of the objectives are linear functions and some
technical linear restrictions are satisfied, then it is defined as a multi objective linear fractional
programming problem MOLFPP in this research. The transformation characteristics are illustrated and the
solution procedure and numerical example are presented.
Exascale Computing for Autonomous DrivingLevent GĂŒrel
Â
Autonomous driving is one of the most computationally demanding technologies of modern times. Report here is a high-level roadmap towards satisfying the ever-increasing
computational needs and requirements of autonomous mobility that is easily at exascale level. We demonstrate that hardware solutions alone will not be sufficient, and exascale computing demands should be met with a combination of hardware and software. In that context, algorithms with reduced computational complexities will be crucial to provide software solutions that will be integrated with available hardware, which is also limited by various mobility restrictions, such as power, space, and weight.
Duality Theory in Multi Objective Linear Programming Problemstheijes
Â
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
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