%-------------------------------------------------------------------------------
% UF Sparse Matrix Collection, Tim Davis
% http://www.cise.ufl.edu/research/sparse/matrices/VDOL/freeFlyingRobot_11
% name: VDOL/freeFlyingRobot_11
% [freeFlyingRobot optimal control problem (matrix 11 of 16)]
% id: 2683
% date: 2015
% author: B. Senses, A. Rao
% ed: T. Davis
% fields: name title A b id date author ed kind notes aux
% aux: rowname mapping
% kind: optimal control problem
%-------------------------------------------------------------------------------
% notes:
% Optimal control problem, Vehicle Dynamics & Optimization Lab, UF       
% Anil Rao and Begum Senses, University of Florida                       
% http://vdol.mae.ufl.edu                                                
%                                                                        
% This matrix arises from an optimal control problem described below.    
% Each optimal control problem gives rise to a sequence of matrices of   
% different sizes when they are being solved inside GPOPS, an optimal    
% control solver created by Anil Rao, Begum Senses, and others at in VDOL
% lab at the University of Florida.  This is one of the matrices in one  
% of these problems.  The matrix is symmetric indefinite.                
%                                                                        
% Rao, Senses, and Davis have created a graph coarsening strategy        
% that matches pairs of nodes.  The mapping is given for this matrix,    
% where map(i)=k means that node i in the original graph is mapped to    
% node k in the smaller graph.  map(i)=map(j)=k means that both nodes    
% i and j are mapped to the same node k, and thus nodes i and j have     
% been merged.                                                           
%                                                                        
% This matrix consists of a set of nodes (rows/columns) and the          
% names of these rows/cols are given                                     
%                                                                        
% Anil Rao, Begum Sense, and Tim Davis, 2015.                            
%                                                                        
% VDOL/freeFlyingRobot                                                   
%                                                                        
% Free flying robot optimal control problem is taken from                
% Ref.~\cite{sakawa1999trajectory}. Free flying robot technology is      
% expected to play an important role in unmanned space missions.         
% Although NASA currently has free flying robots, called spheres,        
% inside the International Space Station (ISS), these free flying        
% robots have neither the technology nor the hardware to complete        
% inside and outside inspection and maintanance. NASA's new plan is to   
% send new free flying robots to ISS that are capable of completing      
% housekeeping of ISS during off hours and working in extreme            
% environments for the external maintanance of ISS. As a result, the     
% crew in ISS can have more time for science experiments. The current    
% free flying robots in ISS works are equipped with a propulsion         
% system. The goal of the free flying robot optimal control problem is   
% to determine the state and the control that minimize the magnitude of  
% thrust during a mission. The state of the system is defined by the     
% inertial coordinates of the center of gravity, the corresponding       
% velocity, thrust direction, and the anglular velocity and the control  
% is the thrust from two engines. The specified accuracy tolerance of    
% $10^{-6}$ were satisfied after eight mesh iterations. As the mesh      
% refinement proceeds, the size of the KKT matrices increases from 798   
% to 6078.                                                               
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