An Efficient Algorithm for Large-Scale Nonlinear Programming Problems with Simple Bounds on the Variables

1998 ◽  
Vol 8 (2) ◽  
pp. 532-560 ◽  
Author(s):  
R. Pytlak
Keyword(s):  
Nonlinear Programming ◽  
Efficient Algorithm ◽  
Large Scale ◽  
Simple Bounds

scholarly journals Large-Scale Linear RankSVM

2014 ◽  
Vol 26 (4) ◽  
pp. 781-817 ◽  
Author(s):  
Ching-Pei Lee ◽  
Chih-Jen Lin
Keyword(s):  
Decision Trees ◽  
Computational Efficiency ◽  
Efficient Algorithm ◽  
Large Scale ◽  
Learning To Rank ◽  
Gradient Boosting ◽  
Baseline Model ◽  
Nonlinear Methods ◽  
Advantages And Disadvantages ◽  
Linear Ranksvm

Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.

A Modification of Feasible Direction Optimization Method for Handling Equality Constraints

1988 ◽  
Author(s):  
Yong Chen ◽  
Bailin Li
Keyword(s):  
Nonlinear Programming ◽  
Efficient Algorithm ◽  
Optimization Method ◽  
Equality Constraints ◽  
Feasible Region ◽  
Feasible Direction ◽  
Feasible Direction Method ◽  
Traditional Algorithm ◽  
Inequality Type ◽  
Nonlinear Equality

Abstract The Feasible Direction Method of Zoutendijk has proven to be one of the efficient algorithm currently available for solving nonlinear programming problems with only inequality type constraints. Since in the case of having equality type constraints, there does not exist nonzero direction close to the feasible region, the traditional algorithm can not work properly. In this paper, a modified feasible direction finding technique has been proposed in order to handle nonlinear equality constraints for the Feasible Direction Method. The algorithm is based on searching along directions intersecting the tangent of the equality constraints at some angle which makes the move toward the interior of the feasible region.

Simulation of Power Line Communication Using OPNET for Vertical Fish Farm

2018 ◽  
pp. 139-164
Keyword(s):  
Efficient Algorithm ◽  
Large Scale ◽  
Network Simulation ◽  
Power Line ◽  
Fish Farm ◽  
Simulation Tool ◽  
Test Bed ◽  
Simulation Test ◽  
Large Scale System ◽  
Large Scale Simulations

OPNET is a network simulation tool which can simulate various elements in a network. It is able to analyze traffics and even can simulate security events recently. System designers can identify vulnerabilities or find an efficient algorithm/protocol that could reduce costs if simulations are performed prior to actual implementation/construction of a large-scale system. Thus, this chapter includes the method of simulating a PLC-system, designing a PLC-based vertical fish farm and an expandable simulation test bed with which students or researchers will be able simulate their implementation methods. With these methods, it will be possible to perform large-scale simulations.

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