TAILIEUCHUNG - Scarf’s generalization of linear complementarity problem revisited

In this paper, we revisit Scarf’s generalization of the linear complementarity problem, formulate this as a vertical linear complementarity problem and obtain some new results on this generalization. Also, a neural network model for solving Scarf’s generalized complementarity problems is proposed. Numerical simulation results show that the proposed model is feasible and efficient. | Yugoslav Journal of Operations Research 23 (2013) Number 2, 143-161 DOI: SCARF’S GENERALIZATION OF LINEAR COMPLEMENTARITY PROBLEM REVISITED Samir Kumar NEOGY Indian Statistical Institute 7, . Sansanwal Marg New Delhi-110016, India skn@ Sagnik SINHA Jadavpur University Kolkata-700032, India sagnik62@ Arup Kumar DAS Indian Statistical Institute 203, B. T. Road Kolkata-700108, India akdas@ Abhijit GUPTA Indian Statistical Institute 203, B. T. Road Kolkata-700108, India agupta@ Received: February 2013/ Accepted: June 2013 Abstract: In this paper, we revisit Scarf’s generalization of the linear complementarity problem, formulate this as a vertical linear complementarity problem and obtain some new results on this generalization. Also, a neural network model for solving Scarf’s generalized complementarity problems is proposed. Numerical simulation results show that the proposed model is feasible and efficient. Keywords: Scarf’s complementarity problem, Vertical linear complementarity problem, CottleDantzig’s algorithm, Lemke’s algorithm, Neural network approach. MSC: 90C33, 92B20. 144 S. K. Neogy, S. Sinha, A. K. Das and A. Gupta / Scarf’s Generalization 1. INTRODUCTION Given a matrix M ∈ R n× n and a vector q ∈ R n , the linear complementarity problem denoted by LCP (q, M ) , is to find w ∈ R n and z ∈ R n such that w − Mz = q, w ≥ 0, z ≥ 0 () wt z = 0 . () LCP is normally identified as a problem of mathematical programming and provides a unifying framework for several optimization problems. For recent books on this problem and applications see Cottle, Pang and Stone [3] and the references cited therein. Scarf [23] introduced a generalization of the linear complementarity problem to accomodate more complicated real life problems as well as to diversify the field of applications. In this paper, we consider a generalization by Scarf, known as Scarf’s generalized linear .

• Toán học
• Scarf’s complementarity problem
• Vertical linear complementarity problem
• CottleDantzig’s algorithm
• Lemke’s algorithm
• Neural network approach
• Wind turbine systems
• Artificial neural network
• Turbine systems operational state
• In depth understanding
• BMC Bioinformatics
• Cryo EM
• Particle recognition
• Convolutional neural network
• Deep learning
• Single particle reconstruction
• Data driven approach
• Equivalent inclusion
• Effective elastic moduli
• Heterogeneous media
• Multi task learning
• Convolutional neural networks
• Named entity recognition
• Biomedical text mining
• RNA binding protein
• CLIP seq
• Deep belief network
• Multimodal deep learning
• Secondary structure prediction
• Complex valued relaxation network
• Inhibitor peptides
• Efficient learning
• Protein structure
• Electronic Engineering
• Fuzzy logic
• Complex systems
• Network traffic control
• Fuzzy Neural Network
• Genetic Algorithm
• Takagi Sugeno approach
• báo cáo hóa học
• công trình nghiên cứu về hóa học
• tài liệu về hóa học
• cách trình bày báo cáo
• Depth map
• Ground plane
• Path direction
• Always Move Straight to the Destination
• Dynamic Window Approach
• Artificial neural network
• Knowledge combination
• Data transformation
• Continous learning
• Neural network
• Individual training datasets
• Proteins perform
• Amino acid
• Protein location
• 3D information
• Deep neural network
• Mobile robot
• Object detection
• Stereo camera
• Landmark based slam
• Non destructive evaluation
• Eddy current technique
• Feature extraction
• Multi frequency approach
• Aeronautical industry
• Model updating
• Grey wolf optimizer
• Hybrid approach
• Modal flexibility
• Damage identification
• BMC Cancer
• Esophageal squamous cell carcinoma
• Machine learning
• Support vector machine
• Random forest
• Logical regression