Internal:Adaptive CDT3D - Smoothing - Revision history

Ctsolakis at 19:32, 4 September 2019

2019-09-04T19:32:58Z

← Older revision		Revision as of 14:32, 4 September 2019
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			= Current Approach =
			The current (as of 2019) approach of the '''adaptive''' code is vertex based smoothing. Similar code to the one we use in
			boundary recovery.

			= Original Approach =
			Original CDT3D approach was a element based global smoothing
	= NASA/refine =		= NASA/refine =
	== Pseudocode ==		== Pseudocode ==

Ctsolakis: /* 27th IMR */

2018-10-05T19:08:36Z

27th IMR

← Older revision		Revision as of 14:08, 5 October 2018
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	'' We evaluate the vertex quality function with a greedy algorithm on a discretized domain. No derivatives are necessary which allows easy replacement of the quality metric ''		'' We evaluate the vertex quality function with a greedy algorithm on a discretized domain. No derivatives are necessary which allows easy replacement of the quality metric ''

	See page 6 figures and page 7 algorithm 2 <ref> Discrete Mesh Optimization on GPU, Daniel Zint and Roberto Grosso https://project.inria.fr/imr27/files/2018/09/1003.pdf </ref>		See page 6 figures and page 7 algorithm 2 <ref> Discrete Mesh Optimization on GPU, Daniel Zint and Roberto Grosso [https://project.inria.fr/imr27/files/2018/09/1003.pdf] </ref>

	= References =		= References =

Ctsolakis: /* Pseudocode */

2018-10-05T19:08:27Z

Pseudocode

← Older revision		Revision as of 14:08, 5 October 2018
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	4. If number of active elements >= 4 exit		4. If number of active elements >= 4 exit
	4.1 else if number of active elements ==1 set dir=grad[worst]		4.1 else if number of active elements ==1 set dir=grad[worst]
	4.2 else use Charalambous and Conn approach <ref> An Efficient Method to Solve the Minmax Problem Directly, C. Charalambous and A. R. Conn, https://epubs.siam.org/doi/abs/10.1137/0715011,http://www.cs.odu.edu/crtc/internal/Christos/Charalambous_An_Efficient_Method_To_Solve_The_Minmax_problem_directly.pdf </ref> to find best dir		4.2 else use Charalambous and Conn approach <ref> An Efficient Method to Solve the Minmax Problem Directly, C. Charalambous and A. R. Conn, [https://epubs.siam.org/doi/abs/10.1137/0715011],[http://www.cs.odu.edu/crtc/internal/Christos/Charalambous_An_Efficient_Method_To_Solve_The_Minmax_problem_directly.pdf] </ref> to find best dir
	5. set m0 = dot(dir,grad[worst])		5. set m0 = dot(dir,grad[worst])
	6. find stepsize alpha:		6. find stepsize alpha:
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	///to approximate all of the quality functions as lines,		///to approximate all of the quality functions as lines,
	//so we will have to do a line search to find our ultimate		//so we will have to do a line search to find our ultimate
	//step size. <ref> Tetrahedral Mesh Improvement Using Swapping and Smoothing, Lori A. Freitag and Carl Ollivier-Gooch, https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291097-0207%2819971115%2940%3A21%3C3979%3A%3AAID-NME251%3E3.0.CO%3B2-9, http://www.cs.odu.edu/crtc/internal/Christos/Freitag_et_al-1997-International_Journal_for_Numerical_Methods_in_Engineering.pdf </ref>		//step size. <ref> Tetrahedral Mesh Improvement Using Swapping and Smoothing, Lori A. Freitag and Carl Ollivier-Gooch, [https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291097-0207%2819971115%2940%3A21%3C3979%3A%3AAID-NME251%3E3.0.CO%3B2-9], [http://www.cs.odu.edu/crtc/internal/Christos/Freitag_et_al-1997-International_Journal_for_Numerical_Methods_in_Engineering.pdf] </ref>
	7. compute the approximation of when this funnction will become the objective /		7. compute the approximation of when this funnction will become the objective /
	8. for each non active incident : alpha = (quals[worst] - quals[i]) / (m1 - m0); min(min_alpha,alpha)		8. for each non active incident : alpha = (quals[worst] - quals[i]) / (m1 - m0); min(min_alpha,alpha)

Ctsolakis: /* 27th IMR */

2018-10-05T19:07:28Z

27th IMR

← Older revision		Revision as of 14:07, 5 October 2018
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	'' We evaluate the vertex quality function with a greedy algorithm on a discretized domain. No derivatives are necessary which allows easy replacement of the quality metric ''		'' We evaluate the vertex quality function with a greedy algorithm on a discretized domain. No derivatives are necessary which allows easy replacement of the quality metric ''

	See page 6 figures and page 7 algorithm 2 https://project.inria.fr/imr27/files/2018/09/1003.pdf		See page 6 figures and page 7 algorithm 2 <ref> Discrete Mesh Optimization on GPU, Daniel Zint and Roberto Grosso https://project.inria.fr/imr27/files/2018/09/1003.pdf </ref>

	= References =		= References =

Ctsolakis: /* 27th IMR */

2018-10-05T19:06:55Z

27th IMR

← Older revision		Revision as of 14:06, 5 October 2018
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	= 27th IMR =		= 27th IMR =
	See page 6 https://project.inria.fr/imr27/files/2018/09/1003.pdf		'' We evaluate the vertex quality function with a greedy algorithm on a discretized domain. No derivatives are necessary which allows easy replacement of the quality metric ''

			See page 6 figures and page 7 algorithm 2 https://project.inria.fr/imr27/files/2018/09/1003.pdf

	= References =		= References =

Ctsolakis at 18:58, 5 October 2018

2018-10-05T18:58:10Z

← Older revision		Revision as of 13:58, 5 October 2018
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	= 27th IMR =		= 27th IMR =
	See page 6 https://project.inria.fr/imr27/files/2018/09/1003.pdf		See page 6 https://project.inria.fr/imr27/files/2018/09/1003.pdf

			= References =

Ctsolakis at 18:57, 5 October 2018

2018-10-05T18:57:45Z

← Older revision		Revision as of 13:57, 5 October 2018
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	''' Code ''' : https://github.com/nasa/refine/blob/master/src/ref_smooth.c#L1258		''' Code ''' : https://github.com/nasa/refine/blob/master/src/ref_smooth.c#L1258

			= 27th IMR =
			See page 6 https://project.inria.fr/imr27/files/2018/09/1003.pdf

Ctsolakis: /* Pseudocode */

2018-10-05T18:57:05Z

Pseudocode

← Older revision		Revision as of 13:57, 5 October 2018
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	= NASA/refine =		= NASA/refine =
	== Pseudocode ==		== Pseudocode ==
	'' We solve the nonsmooth optimization problem		'' We solve the nonsmooth optimization problem...
	Using this information, we can predict the points at which the active set A will change by		...Using this information, we can predict the points at which the active set A will change by
	computing the intersection of each linear approximation with the pro jection of the current		computing the intersection of each linear approximation with the pro jection of the current
	active function in the search direction.		active function in the search direction. The distance to the nearest intersection point from the current location gives the initial step length, a.
	The distance to the nearest intersection point from the current location gives the initial step length, a.		''
			'' The initial step is accepted if the actual improvement exceeds 90 percent of the estimated improvement or the subsequent step results in a smaller function improvement. Otherwise is halved recursively until a step is accepted or falls below some minimum step length tolerance.''
	The initial step is accepted if the actual
	improvement exceeds 90 percent of the estimated improvement or the subsequent step results
	in a smaller function improvement. Otherwise is halved recursively until a step is accepted
	or falls below some minimum step length tolerance.''

	1. Evaluate the degree of the node and collect adajcent cells		1. Evaluate the degree of the node and collect adajcent cells

Ctsolakis: /* Pseudocode */

2018-10-05T18:56:20Z

Pseudocode

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 = NASA/refine =
 == Pseudocode ==
 . Evaluate the degree of the node and collect adajcent cells

Ctsolakis: /* NASA/refine */

2018-10-05T18:54:37Z

NASA/refine

← Older revision		Revision as of 13:54, 5 October 2018
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	///to approximate all of the quality functions as lines,		///to approximate all of the quality functions as lines,
	//so we will have to do a line search to find our ultimate		//so we will have to do a line search to find our ultimate
	//step size.		//step size. <ref> Tetrahedral Mesh Improvement Using Swapping and Smoothing, Lori A. Freitag and Carl Ollivier-Gooch, https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291097-0207%2819971115%2940%3A21%3C3979%3A%3AAID-NME251%3E3.0.CO%3B2-9, http://www.cs.odu.edu/crtc/internal/Christos/Freitag_et_al-1997-International_Journal_for_Numerical_Methods_in_Engineering.pdf </ref>
	7.compute the approximation of when this funnction will become the objective /		7. compute the approximation of when this funnction will become the objective /
	8. for each non active incident : alpha = (quals[worst] - quals[i]) / (m1 - m0); min(min_alpha,alpha)		8. for each non active incident : alpha = (quals[worst] - quals[i]) / (m1 - m0); min(min_alpha,alpha)
	9. if min_alpha didn’t change min_alpha = (1.0 - quals[0]) / ref_math_dot(dir, &(grads[3 * 0]))		9. if min_alpha didn’t change min_alpha = (1.0 - quals[0]) / ref_math_dot(dir, &(grads[3 * 0]))
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	''' Code ''' : https://github.com/nasa/refine/blob/master/src/ref_smooth.c#L1258		''' Code ''' : https://github.com/nasa/refine/blob/master/src/ref_smooth.c#L1258

	* smoothing : [https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291097-0207%2819971115%2940%3A21%3C3979%3A%3AAID-NME251%3E3.0.CO%3B2-9 Tetrahedral Mesh Improvement Using Swapping and Smoothing, Lori A. Freitag and Carl Ollivier-Gooch ] [http://www.cs.odu.edu/crtc/internal/Christos/Freitag_et_al-1997-International_Journal_for_Numerical_Methods_in_Engineering.pdf PDF]