OQNLP - Revision history

Elias at 15:59, 22 January 2012

2012-01-22T15:59:37Z

← Older revision		Revision as of 10:59, 22 January 2012
Line 37:		Line 37:
	The OQNLP and MSNLP (LSGRG2) solvers are accessed via the ''tomRun ''driver routine, which calls the appropriate interface routine (for example ''oqnlpTL''). The solvers themselves are located in a set of MEX files: ''oqnlp'', ''msnlp ''and ''lsgrg2''.		The OQNLP and MSNLP (LSGRG2) solvers are accessed via the ''tomRun ''driver routine, which calls the appropriate interface routine (for example ''oqnlpTL''). The solvers themselves are located in a set of MEX files: ''oqnlp'', ''msnlp ''and ''lsgrg2''.

	~~<figtable id~~=~~"tab~~:~~interfaceRoutines">~~		====Table: The interface routines.====
	{\|class="wikitable"
	~~\|+<caption>The interface routines.</caption>~~		{\|class="wikitable
	!Function\|\|Description		!Function\|\|Description
	\|-valign="top"		\|-valign="top"
Line 60:		Line 60:
	This routine then calls the MEX file ''lsgrg''2		This routine then calls the MEX file ''lsgrg''2
	\|}		\|}
	~~</figtable>~~

	==Setting OQNLP Options==		==Setting OQNLP Options==

Elias at 15:17, 17 December 2011

2011-12-17T15:17:08Z

← Older revision		Revision as of 10:17, 17 December 2011
Line 99:		Line 99:
	<math>		<math>
	\begin{array}{ccccccl}		\begin{array}{ccccccl}
	\min\limits_{x} & ~~\multicolumn{5}{l}{~~f(x)} & \\		\min\limits_{x} & f(x) & \\
	s/t & x_L & \leq & x & \leq & x_U & \\		s/t & x_L & \leq & x & \leq & x_U & \\
	{} & b_L & \leq & Ax & \leq & b_U & \\		{} & b_L & \leq & Ax & \leq & b_U & \\
Line 236:		Line 236:
	<math>		<math>
	\begin{array}{ccccccl}		\begin{array}{ccccccl}
	\min\limits_{x} & ~~\multicolumn{5}{l}{~~f(x)} & \\		\min\limits_{x} & f(x) & \\
	\\		\\
	s/t & x_L & \leq & x & \leq & x_U & \\		s/t & x_L & \leq & x & \leq & x_U & \\
Line 366:		Line 366:
	<math>		<math>
	\begin{array}{ccccccl}		\begin{array}{ccccccl}
	\min\limits_{x} & ~~\multicolumn{5}{l}{~~f(x)} & \\		\min\limits_{x} & f(x) & \\
	\\		\\
	s/t & x_L & \leq & x & \leq & x_U & \\		s/t & x_L & \leq & x & \leq & x_U & \\

Elias: /* OQNLP and MSNLP options */

2011-10-17T13:49:06Z

OQNLP and MSNLP options

← Older revision		Revision as of 08:49, 17 October 2011
Line 501:		Line 501:
	\|''DYNAMIC_MERIT_FILTER''\|\|A value of 1 turns on logic which dynamically varies the pa- 1 rameters which increases the merit filter threshold, THRESH- OLD INCREASE FACTOR. If MERIT WAITCYCLE consecutive trial points have been rejected by the merit filter, this value is replaced by max(THRESHOLD INCREASE FACTOR, val), where val is the value of THRESHOLD INCREASE FACTOR which causes the merit filter to just accept the best of the previous MERIT WAITCYCLE trial points. A value of 0 turns off this logic. Turning it off can reduce NLP solver calls, but may lead to a worse final solution.\|\|1		\|''DYNAMIC_MERIT_FILTER''\|\|A value of 1 turns on logic which dynamically varies the pa- 1 rameters which increases the merit filter threshold, THRESH- OLD INCREASE FACTOR. If MERIT WAITCYCLE consecutive trial points have been rejected by the merit filter, this value is replaced by max(THRESHOLD INCREASE FACTOR, val), where val is the value of THRESHOLD INCREASE FACTOR which causes the merit filter to just accept the best of the previous MERIT WAITCYCLE trial points. A value of 0 turns off this logic. Turning it off can reduce NLP solver calls, but may lead to a worse final solution.\|\|1
	\|-valign="top"		\|-valign="top"
	''FEASIBILITY_TOLERANCE''\|\|This tolerance is used to check each point returned by an NLP solver for feasibility. If the largest absolute infeasibility at the point is larger than this tolerance, the point is classified infeasible. This test is made because points returned by NLP solvers may occasionally be infeasible despite feasible status codes. Some NLP solvers use internal scaling before testing for feasibility. The unscaled problem may be infeasible, while the scaled one is feasible. If this occurs, increasing this tolerance (to 1.e-2 or larger) often eliminates the problem.\|\|1.e-4		\|''FEASIBILITY_TOLERANCE''\|\|This tolerance is used to check each point returned by an NLP solver for feasibility. If the largest absolute infeasibility at the point is larger than this tolerance, the point is classified infeasible. This test is made because points returned by NLP solvers may occasionally be infeasible despite feasible status codes. Some NLP solvers use internal scaling before testing for feasibility. The unscaled problem may be infeasible, while the scaled one is feasible. If this occurs, increasing this tolerance (to 1.e-2 or larger) often eliminates the problem.\|\|1.e-4
	\|-valign="top"		\|-valign="top"
	\|''FEASIBILITY_MODE''\|\|If this option is set to 1 the system focuses on finding a feasible point, 0 stopping after the first NLP solver call which finds such a point. If set to 0, the system tries to find the global optimum of the objective function subject to the constraints.\|\|0		\|''FEASIBILITY_MODE''\|\|If this option is set to 1 the system focuses on finding a feasible point, 0 stopping after the first NLP solver call which finds such a point. If set to 0, the system tries to find the global optimum of the objective function subject to the constraints.\|\|0

Elias: /* Overview */

2011-10-17T13:47:43Z

Overview

← Older revision		Revision as of 08:47, 17 October 2011
Line 11:		Line 11:
	LSGRG2 can be called directly. The solver does not find global optima but only local solutions, which may be sufficient for many user cases. Up to 10,000 variables and constraints are supported		LSGRG2 can be called directly. The solver does not find global optima but only local solutions, which may be sufficient for many user cases. Up to 10,000 variables and constraints are supported

	The multistart feature calls an NLP solver with a different set of initial values and return the feasible solutions as well as the optimal point. The starting points are calculated from scatter search algorithm, see [http://www.opttek.com/ ~~http://www. ] [~~http://www.opttek.com/ ~~opttek.com~~] for additional information. The user may also choose to use uniformly distributed initial values. Neither of the two options guarantee that a global optimum is obtained, however the likelihood is high.		The multistart feature calls an NLP solver with a different set of initial values and return the feasible solutions as well as the optimal point. The starting points are calculated from scatter search algorithm, see [http://www.opttek.com/ http://www.opttek.com/] for additional information. The user may also choose to use uniformly distributed initial values. Neither of the two options guarantee that a global optimum is obtained, however the likelihood is high.

	The main advantage with OQNLP/MSNLP for smooth problems is that good local solutions are easily obtained, and that integer variables are handled.		The main advantage with OQNLP/MSNLP for smooth problems is that good local solutions are easily obtained, and that integer variables are handled.

Elias: Created page with "==Introduction== ===Overview=== Welcome to the TOMLAB /OQNLP User's Guide. The package includes the OQNLP solvers from Optimal Methods Inc. and an interface to The Math..."

2011-10-17T13:30:21Z

Created page with "==Introduction== ===Overview=== Welcome to the TOMLAB /OQNLP User's Guide. The package includes the OQNLP solvers from Optimal Methods Inc. and an interface to The Math..."

Show changes