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Nov 29, 2015 at 1:45 vote accept user123
Nov 29, 2015 at 0:36 comment added James @David The DynamicSparsityPattern is the same idea except that there is no compress call after since this sparsity pattern is always compressed. How the DynamicSparsityPattern remains compressed when we call make_sparsity_pattern is not really important for the user to know. I am guessing it does this by many copying of vectors internally but the actual implementation details aren't really important.
Nov 28, 2015 at 11:31 comment added user123 Thank you so much, @James. Your additional explanation on the concrete example are awesome and almost clarifies everything. Could you please also give some comments on the second example of DynimicSparsityPattern to explain how it can be kept being compressed at all times? YOUR EFFORTS ARE GREATLY APPRECIATED!
Nov 23, 2015 at 9:13 history edited James CC BY-SA 3.0
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Nov 23, 2015 at 9:07 history edited James CC BY-SA 3.0
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Nov 23, 2015 at 9:02 history edited James CC BY-SA 3.0
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Nov 23, 2015 at 8:17 history edited James CC BY-SA 3.0
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Nov 23, 2015 at 8:02 comment added James @David406 You call make_sparsity_pattern first and then you compress. Once you have called make_sparsity_pattern you know exactly where the non-zeros will be.
Nov 23, 2015 at 7:35 comment added user123 Hi James, thanks for your detailed answer! It's really a great help. However, as to your third point, I might disagree with it. At the time I call sparcity_pattern.compress(), information about the actual nonzero length at each row is in fact still unknown. Then how can it determine the extent to compress, based on what?
Nov 23, 2015 at 6:58 history edited James CC BY-SA 3.0
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Nov 22, 2015 at 18:52 history edited James CC BY-SA 3.0
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Nov 22, 2015 at 18:45 history edited James CC BY-SA 3.0
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Nov 22, 2015 at 18:39 history edited James CC BY-SA 3.0
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Nov 22, 2015 at 18:34 history answered James CC BY-SA 3.0