On the average speed of Lemke's algorithm for quadratic programming

1986 ◽  
Vol 35 (2) ◽  
pp. 236-246 ◽  
Author(s):  
Yieh-Hei Wan
Keyword(s):  
Quadratic Programming ◽  
Average Speed ◽  
Lemke's Algorithm

Subtle Impact of Akt1 and Akt3 on Exploratory Behavior in Gene Targeted Mice

2015 ◽  
Vol 223 (3) ◽  
pp. 173-180 ◽  
Author(s):  
Christina Leibrock ◽  
Michael Hierlmeier ◽  
Undine E. Lang ◽  
Florian Lang
Keyword(s):  
Forced Swimming Test ◽  
Open Field Test ◽  
Wild Type ◽  
Average Speed ◽  
Closed Area ◽  
Light Area ◽  
Swimming Test ◽  
The Impact ◽  
Center Area ◽  
Dark Transition

Abstract. The present study explored the impact of Akt1 and Akt3 on behavior. Akt1 (akt1-/-) and Akt3 (akt3-/-) knockout mice were compared to wild type (wt) mice. The akt1-/- mice, akt3-/- mice, and wt mice were similar in most parameters of the open-field test. However, the distance traveled in the center area was slightly but significantly less in akt3-/- mice than in wt mice. In the light/dark transition test akt1-/- mice had significantly lower values than wt mice and akt3-/- mice for distance traveled, number of rearings, rearing time in the light area, as well as time spent and distance traveled in the entrance area. They were significantly different from akt3-/- mice in the distance traveled, visits, number of rearings, rearing time in the light area, as well as time spent, distance traveled, number of rearings, and rearing time in the entrance area. In the O-maze the time spent, and the visits to open arms, as well as the number of protected and unprotected headdips were significantly less in akt1-/- mice than in wt mice, whereas the time spent in closed arms was significantly more in akt1-/- mice than in wt mice. Protected and unprotected headdips were significantly less in akt3-/- mice than in wt mice. In closed area, akt3-/- mice traveled a significantly larger distance at larger average speed than akt1-/- mice. No differences were observed between akt1-/- mice, akt3-/- mice and wt-type mice in the time of floating during the forced swimming test. In conclusion, akt1-/- mice and less so akt3-/ mice display subtle changes in behavior.

An Iterated Greedy Algorithm for the Binary Quadratic Programming Problem

2010 ◽  
Vol 130 (6) ◽  
pp. 1089-1090
Author(s):  
Takenori Murakami ◽  
Fubito Toyama ◽  
Kenji Shoji ◽  
Jyuichi Miyamichi
Keyword(s):  
Quadratic Programming ◽  
Programming Problem ◽  
Greedy Algorithm ◽  
Quadratic Programming Problem ◽  
Binary Quadratic Programming ◽  
Iterated Greedy Algorithm ◽  
Iterated Greedy

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

Improved SDP Bounds for Binary Quadratic Programming

2018 ◽  
Vol 04 (06) ◽  
Author(s):  
Baiyi Wu ◽  
Keyword(s):  
Quadratic Programming ◽  
Binary Quadratic Programming

Barrier Methods for Large-Scale Quadratic Programming

1991 ◽  
Author(s):  
Dulce B. Ponceleon
Keyword(s):  
Quadratic Programming ◽  
Large Scale ◽  
Barrier Methods

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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