scholarly journals Solving Reduced KKT Systems in Barrier Methods for Linear and Quadratic Programming

1991 ◽  
Author(s):  
Philip E. Gill ◽  
Walter Murray ◽  
Dulce B. Ponceleon ◽  
Michael A. Saunders
Keyword(s):  
Quadratic Programming ◽  
Barrier Methods ◽  
Kkt Systems

scholarly journals Updating Constraint Preconditioners for KKT Systems in Quadratic Programming Via Low-Rank Corrections

2015 ◽  
Vol 25 (3) ◽  
pp. 1787-1808 ◽  
Author(s):  
Stefania Bellavia ◽  
Valentina De Simone ◽  
Daniela di Serafino ◽  
Benedetta Morini
Keyword(s):  
Quadratic Programming ◽  
Low Rank ◽  
Kkt Systems

INTERVAL WEIGHTS IN AHP BY LINEAR AND QUADRATIC PROGRAMMING

2005 ◽  
Vol 10 (02) ◽  
Author(s):  
T. Entani ◽  
K. Sugihara ◽  
H. Tanaka
Keyword(s):  
Quadratic Programming

scholarly journals Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2400
Author(s):  
Ziyong Zhang ◽  
Xiaoling Xu ◽  
Jinqiang Cui ◽  
Wei Meng
Keyword(s):  
Quadratic Programming ◽  
Wide Band ◽  
Area Coverage ◽  
Localization Algorithm ◽  
Placement Problem ◽  
Position Information ◽  
Relative Localization ◽  
Coverage Control ◽  
Localization Algorithms ◽  
Multi Uav

This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task. In this paper, we propose a single-landmark-based relative localization algorithm, combined with a distributed coverage control law. At the same time, the optimal multi-UAV placement problem was formulated as a quadratic programming problem by compromising between optimal relative localization and optimal coverage control and was solved by using Sequential Quadratic Programming (SQP) algorithms. Simulation results show that our proposed method can guarantee that a team of UAVs can efficiently localize themselves in a cooperative manner and, at the same time, complete the area coverage task.

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