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<DIV><FONT face=Arial size=2>Hi,</FONT></DIV>
<DIV><FONT face=Arial size=2> I am currently using CLP as a
part of my research project. I have a collection of LP problems and try
to solve them as fast as possible. So I try to exploite the relations
among LPs tasks to speed up the performance. Now I have the following
question:</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2> 1. If there are two LP tasks,
The second only has one more row constraint than the first one, how can I
speed up the second LP after the first one is done? </FONT></DIV>
<DIV><FONT face=Arial size=2>
This question has already been answered by John (Thanks!). We
can simply add one more row and use dual algorithm. Then the second LP will be a
lot faster.</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2> 2. If I have a collection of LP,
all of them shares the same matrix A, the column lower bound, and column higher
bound. The only difference are the row lower bound and higher bound. How
can I take advantage of this fact to minimize the total cost of all these LP
tasks.</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2> 3. Since the solution space of a
LP is convex. Is there any fast way to find all the extreme points of the convex
solution space?</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2> I know that some of the question
might seen to be amateurish. My research major is in Database rather than
Optimization. But I really appreciate if some one can give me any clue about the
above question. Thanks in advance.</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>Lei Chen</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>
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