% opening :/home/verlaanm/deltares/src/openda_1_20100401/public/tests/waqua_usedoc_rough_stat9/./stochobserver/obs_rough_20000707.xml
% Starting Algorithm: 
%	className: org.openda.algorithms.SparseDud
%	dir.: /home/verlaanm/deltares/src/openda_1_20100401/public/tests/waqua_usedoc_rough_stat9/./algorithm
%	config.: sparseDudAlgorithm.xml
%  Algorithm  configstring = sparseDudAlgorithm.xml
% opening :/home/verlaanm/deltares/src/openda_1_20100401/public/tests/waqua_usedoc_rough_stat9/./algorithm/sparseDudAlgorithm.xml
% Application initializing finished
% Initializing application
%  Algorithm  Sparse Dud preparation
%  Algorithm  Retrieving initial parameters from model
%  Algorithm  Starting optimizer
%  Algorithm  costFunction@class=org.openda.algorithms.SimulationKwadraticCostFunction
%  Algorithm  costFunction@weakParameterConstraint=false
%  Algorithm  costFunction@factor=0.5
%  Algorithm  Retrieving initial parameters from model
% predictions -1:-1: 
% predictions 0:100: M230.waterlevel
% predictions 101:201: M260.waterlevel
% predictions 202:302: M290.waterlevel
% predictions 303:403: M320.waterlevel
% predictions 404:504: M350.waterlevel
% predictions 505:605: M380.waterlevel
% predictions 606:706: M410.waterlevel
% predictions 707:807: M440.waterlevel
% I have 9 parameters
%
% I have 808 Predictions
%
% I have found the following patterns:
%    0  1  2  
%    1  2  3  
%    2  3  4  
%    3  4  5  
%    4  5  6  
%    5  6  7  
%    6  7  8  
% I have 9 parameters
%
% I have 808 predictions
%
% search direction 0: [0.02,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]
%
% search direction 1: [0.0,0.02,0.0,0.0,0.0,0.0,0.0,0.0,0.0]
%
% search direction 2: [0.0,0.0,0.02,0.0,0.0,0.0,0.0,0.0,0.0]
%
% search direction 3: [0.0,0.0,0.0,0.02,0.0,0.0,0.0,0.0,0.0]
%
% search direction 4: [0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0,0.0]
%
% search direction 5: [0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0,0.0]
%
% search direction 6: [0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0,0.0]
%
% search direction 7: [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.0]
%
% search direction 8: [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02]
%
% ========================================================================
% prepare no 1
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{1}	=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0];
% ========================================================================
% evaluate no. 1
costObserved{1}	=10.475393981479684;
costTotal{1}	=10.475393981479684;
% SimulationKwadraticCostFunction: evaluation 1 : cost = 10.475
% start evaluation: p=TreeVector Params, SubTreeVectors:
%	TreeVector A-411 [0.022783868081388763]
%	TreeVector A-412 [-0.004161023031763519]
%	TreeVector A-413 [0.009989038351585669]
%	TreeVector A-414 [-0.014243506936728299]
%	TreeVector A-415 [0.010966641080414763]
%	TreeVector A-416 [-0.01358111841769032]
%	TreeVector A-417 [0.007053714429785334]
%	TreeVector A-418 [-0.01236955263205201]
%	TreeVector A-419 [-0.0032303584659622996]
% ========================================================================
% prepare no 2
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{2}	=[0.022783868081388763, -0.004161023031763519, 0.009989038351585669, -0.014243506936728299, 0.010966641080414763, -0.01358111841769032, 0.007053714429785334, -0.01236955263205201, -0.0032303584659622996];
% ========================================================================
% evaluate no. 2
costObserved{2}	=16.54435532969439;
costTotal{2}	=16.54435532969439;
% SimulationKwadraticCostFunction: evaluation 2 : cost = 16.544
% start evaluation: p=TreeVector Params, SubTreeVectors:
%	TreeVector A-411 [-0.016809547057461734]
%	TreeVector A-412 [-0.006261262924794224]
%	TreeVector A-413 [-0.0024581278639413512]
%	TreeVector A-414 [0.0019395285384530376]
%	TreeVector A-415 [0.0018487048479797485]
%	TreeVector A-416 [9.557812786429065E-4]
%	TreeVector A-417 [0.006161404624414096]
%	TreeVector A-418 [-0.004006668222379334]
%	TreeVector A-419 [0.01615523089730852]
% ========================================================================
% prepare no 3
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{3}	=[-0.016809547057461734, -0.006261262924794224, -0.0024581278639413512, 0.0019395285384530376, 0.0018487048479797485, 9.557812786429065E-4, 0.006161404624414096, -0.004006668222379334, 0.01615523089730852];
% ========================================================================
% evaluate no. 3
costObserved{3}	=9.740390131897941;
costTotal{3}	=9.740390131897941;
% SimulationKwadraticCostFunction: evaluation 3 : cost = 9.74
% start evaluation: p=TreeVector Params, SubTreeVectors:
%	TreeVector A-411 [0.0026964786161068916]
%	TreeVector A-412 [0.02118343268101252]
%	TreeVector A-413 [-0.007513589523499057]
%	TreeVector A-414 [0.011324722104285865]
%	TreeVector A-415 [-0.012147120691711411]
%	TreeVector A-416 [0.007112666015462724]
%	TreeVector A-417 [-0.01322859893227705]
%	TreeVector A-418 [0.011093708359705278]
%	TreeVector A-419 [-0.017997799863222867]
% ========================================================================
% prepare no 4
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{4}	=[0.0026964786161068916, 0.02118343268101252, -0.007513589523499057, 0.011324722104285865, -0.012147120691711411, 0.007112666015462724, -0.01322859893227705, 0.011093708359705278, -0.017997799863222867];
% ========================================================================
% evaluate no. 4
costObserved{4}	=15.753588696567682;
costTotal{4}	=15.753588696567682;
% SimulationKwadraticCostFunction: evaluation 4 : cost = 15.754
%  Algorithm  outerLoop@maxIterations=12
%  Algorithm  outerLoop@absTolerance=0.0020
%  Algorithm  outerLoop@relTolerance=0.01
%  Algorithm  lineSearch@maxIterations=2
%  Algorithm  lineSearch@maxRelStepSize=1.0
%  Algorithm  lineSearch/backtracking@startIterationNegativeLook=3
%  Algorithm  lineSearch/backtracking@shorteningFactor=0.5
% Application initialized
% Application starting next step
%  START Sparse DuD outer iteration... 
%  START 2Sparse DuD outer iteration... true
% ======================================================
% Sparse DUD outer iteration no.1
% ======================================================
% -----------------------------------------------------
costs{1}	=[9.740390131897941,10.475393981479684,15.753588696567682,16.54435532969439,1.0E30,1.0E30,1.0E30,1.0E30,1.0E30,1.0E30];
% -----------------------------------------------------
% dimensions: 
% N (number of tries (excluding initial try): 3
% Npar (number of parameters): 9
% Nobs (number of predictions=number of observations): 808
% RHS norm: 4.41370369007661
% size dP_matrix 3 3.
% size xvals should be 3,202
% size xvals 3 202.
% size dP_matrix 3 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 3 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 3 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 3 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 3 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 3 3.
% size xvals should be 3,101
% size xvals 3 101.
% size A 808 9.
% Start search until improvement,
% norm delta_p: 0.036556037537136946
% Next try p=
%   Params - containing:
%      A-411= -8.834E-3
%      A-412= -0.043
%      A-413= 1.039E-3
%      A-414= 0.02
%      A-415= -0.019
%      A-416= -0.012
%      A-417= 2.619E-3
%      A-418= 9.611E-3
%      A-419= 5.227E-3
%   delta_p=    [7.976E-3,-0.037,3.497E-3,0.018,-0.021,-0.013,-3.543E-3,0.014,-0.011]
% ========================================================================
% prepare no 5
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{5}	=[-0.008834022470315471, -0.04281730046193117, 0.0010388214856945554, 0.02042654437301736, -0.0193954471944005, -0.01167043985040637, 0.00261863356559451, 0.009610681155155995, 0.005227355773088726];
% ========================================================================
% evaluate no. 5
costObserved{5}	=4.677878580918254;
costTotal{5}	=4.677878580918254;
% SimulationKwadraticCostFunction: evaluation 5 : cost = 4.678
%      cost =4.677878580918254
% number of stored parameter sets changed from 3 to 4
% stop criterium1: imain > maxit 1<12
% stop criterium2: diff > abstol 5.062511550979687<0.0020
% stop criterium3: diff ; reltol 5.062511550979687<0.04677878580918254
% Application starting next step
%  START Sparse DuD outer iteration... 
%  START 2Sparse DuD outer iteration... true
% ======================================================
% Sparse DUD outer iteration no.2
% ======================================================
% -----------------------------------------------------
costs{2}	=[4.677878580918254,9.740390131897941,10.475393981479684,15.753588696567682,16.54435532969439,1.0E30,1.0E30,1.0E30,1.0E30,1.0E30];
% -----------------------------------------------------
% dimensions: 
% N (number of tries (excluding initial try): 4
% Npar (number of parameters): 9
% Nobs (number of predictions=number of observations): 808
% RHS norm: 3.0587182220395044
% size dP_matrix 4 3.
% size xvals should be 3,202
% size xvals 3 202.
% size dP_matrix 4 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 4 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 4 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 4 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 4 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 4 3.
% size xvals should be 3,101
% size xvals 3 101.
% size A 808 9.
% Start search until improvement,
% norm delta_p: 0.02727468269264651
% Next try p=
%   Params - containing:
%      A-411= 0.018
%      A-412= -0.028
%      A-413= 7.06E-3
%      A-414= 0.034
%      A-415= -0.04
%      A-416= -0.022
%      A-417= -7.143E-3
%      A-418= 0.012
%      A-419= 0.018
%   delta_p=    [0.027,0.015,6.021E-3,0.013,-0.021,-0.01,-9.762E-3,2.002E-3,0.013]
% ========================================================================
% prepare no 6
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{6}	=[0.01844066022233104, -0.028233594719692227, 0.007059809029532996, 0.03387515053056532, -0.04042993973736418, -0.021789349096069242, -0.007143276832882733, 0.01161283320100936, 0.018185395528667134];
% ========================================================================
% evaluate no. 6
costObserved{6}	=0.8023730416306033;
costTotal{6}	=0.8023730416306033;
% SimulationKwadraticCostFunction: evaluation 6 : cost = 0.802
%      cost =0.8023730416306033
% number of stored parameter sets changed from 4 to 5
% stop criterium1: imain > maxit 2<12
% stop criterium2: diff > abstol 3.8755055392876505<0.0020
% stop criterium3: diff ; reltol 3.8755055392876505<0.008023730416306033
% Application starting next step
%  START Sparse DuD outer iteration... 
%  START 2Sparse DuD outer iteration... true
% ======================================================
% Sparse DUD outer iteration no.3
% ======================================================
% -----------------------------------------------------
costs{3}	=[0.8023730416306033,4.677878580918254,9.740390131897941,10.475393981479684,15.753588696567682,16.54435532969439,1.0E30,1.0E30,1.0E30,1.0E30];
% -----------------------------------------------------
% dimensions: 
% N (number of tries (excluding initial try): 5
% Npar (number of parameters): 9
% Nobs (number of predictions=number of observations): 808
% RHS norm: 1.2667857290249234
% size dP_matrix 5 3.
% size xvals should be 3,202
% size xvals 3 202.
% size dP_matrix 5 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 5 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 5 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 5 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 5 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 5 3.
% size xvals should be 3,101
% size xvals 3 101.
% size A 808 9.
% Start search until improvement,
% norm delta_p: 0.013038930206433586
% Next try p=
%   Params - containing:
%      A-411= 0.031
%      A-412= -0.032
%      A-413= 0.013
%      A-414= 0.034
%      A-415= -0.04
%      A-416= -0.022
%      A-417= -4.9E-3
%      A-418= 7.745E-3
%      A-419= 0.018
%   delta_p=    [0.013,-3.745E-3,5.61E-3,2.462E-4,7.3E-4,-1.451E-4,2.243E-3,-3.868E-3,-1.371E-4]
% ========================================================================
% prepare no 7
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{7}	=[0.03147959042876462, -0.03197870777337631, 0.012669393073010949, 0.034121384017018135, -0.039699898464991314, -0.02193446954000453, -0.004899853597317965, 0.007745241411021038, 0.018048297570974016];
% ========================================================================
% evaluate no. 7
costObserved{7}	=0.49721719995488217;
costTotal{7}	=0.49721719995488217;
% SimulationKwadraticCostFunction: evaluation 7 : cost = 0.497
%      cost =0.49721719995488217
% number of stored parameter sets changed from 5 to 6
% stop criterium1: imain > maxit 3<12
% stop criterium2: diff > abstol 0.30515584167572113<0.0020
% stop criterium3: diff ; reltol 0.30515584167572113<0.004972171999548822
% Application starting next step
%  START Sparse DuD outer iteration... 
%  START 2Sparse DuD outer iteration... true
% ======================================================
% Sparse DUD outer iteration no.4
% ======================================================
% -----------------------------------------------------
costs{4}	=[0.49721719995488217,0.8023730416306033,4.677878580918254,9.740390131897941,10.475393981479684,15.753588696567682,16.54435532969439,1.0E30,1.0E30,1.0E30];
% -----------------------------------------------------
% dimensions: 
% N (number of tries (excluding initial try): 6
% Npar (number of parameters): 9
% Nobs (number of predictions=number of observations): 808
% RHS norm: 0.9972133171542408
% size dP_matrix 6 3.
% size xvals should be 3,202
% size xvals 3 202.
% size dP_matrix 6 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 6 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 6 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 6 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 6 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 6 3.
% size xvals should be 3,101
% size xvals 3 101.
% size A 808 9.
% Start search until improvement,
% norm delta_p: 0.0034999434868133255
% Next try p=
%   Params - containing:
%      A-411= 0.033
%      A-412= -0.032
%      A-413= 0.016
%      A-414= 0.036
%      A-415= -0.038
%      A-416= -0.022
%      A-417= -2.944E-3
%      A-418= 7.425E-3
%      A-419= 0.018
%   delta_p=    [1.232E-3,5.753E-5,3.5E-3,1.432E-3,1.218E-3,-3.64E-4,1.956E-3,-3.204E-4,4.499E-5]
% ========================================================================
% prepare no 8
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{8}	=[0.03271151029545731, -0.03192117678245806, 0.016169336559824273, 0.03555306957300443, -0.03848189077347948, -0.022298506316891595, -0.0029442297933564574, 0.00742482328855373, 0.018093290039911482];
% ========================================================================
% evaluate no. 8
costObserved{8}	=0.3069908765780312;
costTotal{8}	=0.3069908765780312;
% SimulationKwadraticCostFunction: evaluation 8 : cost = 0.307
%      cost =0.3069908765780312
% number of stored parameter sets changed from 6 to 7
% stop criterium1: imain > maxit 4<12
% stop criterium2: diff > abstol 0.19022632337685097<0.0020
% stop criterium3: diff ; reltol 0.19022632337685097<0.003069908765780312
% Application starting next step
%  START Sparse DuD outer iteration... 
%  START 2Sparse DuD outer iteration... true
% ======================================================
% Sparse DUD outer iteration no.5
% ======================================================
% -----------------------------------------------------
costs{5}	=[0.3069908765780312,0.49721719995488217,0.8023730416306033,4.677878580918254,9.740390131897941,10.475393981479684,15.753588696567682,16.54435532969439,1.0E30,1.0E30];
% -----------------------------------------------------
% dimensions: 
% N (number of tries (excluding initial try): 7
% Npar (number of parameters): 9
% Nobs (number of predictions=number of observations): 808
% RHS norm: 0.7835698776472092
% size dP_matrix 7 3.
% size xvals should be 3,202
% size xvals 3 202.
% size dP_matrix 7 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 7 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 7 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 7 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 7 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 7 3.
% size xvals should be 3,101
% size xvals 3 101.
% size A 808 9.
% Start search until improvement,
% norm delta_p: 0.0034907856433479614
% Next try p=
%   Params - containing:
%      A-411= 0.032
%      A-412= -0.031
%      A-413= 0.019
%      A-414= 0.039
%      A-415= -0.035
%      A-416= -0.023
%      A-417= -1.315E-3
%      A-418= 7.863E-3
%      A-419= 0.018
%   delta_p=    [-6.667E-4,6.533E-4,3.044E-3,3.134E-3,3.491E-3,-1.027E-3,1.629E-3,4.381E-4,2.953E-4]
% ========================================================================
% prepare no 9
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{9}	=[0.03204478748625934, -0.03126784902789042, 0.019213688566171998, 0.03868712053791179, -0.03499110513013152, -0.0233259903286089, -0.0013154119664867178, 0.00786296852006459, 0.018388592537645777];
% ========================================================================
% evaluate no. 9
costObserved{9}	=0.11365555670993062;
costTotal{9}	=0.11365555670993062;
% SimulationKwadraticCostFunction: evaluation 9 : cost = 0.114
%      cost =0.11365555670993062
% number of stored parameter sets changed from 7 to 8
% stop criterium1: imain > maxit 5<12
% stop criterium2: diff > abstol 0.1933353198681006<0.0020
% stop criterium3: diff ; reltol 0.1933353198681006<0.0011365555670993061
% Application starting next step
%  START Sparse DuD outer iteration... 
%  START 2Sparse DuD outer iteration... true
% ======================================================
% Sparse DUD outer iteration no.6
% ======================================================
% -----------------------------------------------------
costs{6}	=[0.11365555670993062,0.3069908765780312,0.49721719995488217,0.8023730416306033,4.677878580918254,9.740390131897941,10.475393981479684,15.753588696567682,16.54435532969439,1.0E30];
% -----------------------------------------------------
% dimensions: 
% N (number of tries (excluding initial try): 8
% Npar (number of parameters): 9
% Nobs (number of predictions=number of observations): 808
% RHS norm: 0.47677155265374344
% size dP_matrix 8 3.
% size xvals should be 3,202
% size xvals 3 202.
% size dP_matrix 8 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 8 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 8 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 8 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 8 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 8 3.
% size xvals should be 3,101
% size xvals 3 101.
% size A 808 9.
% Start search until improvement,
% norm delta_p: 0.004475085806362038
% Next try p=
%   Params - containing:
%      A-411= 0.031
%      A-412= -0.03
%      A-413= 0.02
%      A-414= 0.04
%      A-415= -0.031
%      A-416= -0.02
%      A-417= -9.565E-4
%      A-418= 8.843E-3
%      A-419= 0.019
%   delta_p=    [-1.242E-3,1.03E-3,9.131E-4,1.645E-3,4.475E-3,3.069E-3,3.589E-4,9.802E-4,8.576E-4]
% ========================================================================
% prepare no 10
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{10}	=[0.030802662707304227, -0.03023814554638236, 0.020126807848904818, 0.04033237404166234, -0.030516019323769482, -0.02025688658137814, -9.565195968600449E-4, 0.008843162802696855, 0.019246143230605948];
% ========================================================================
% evaluate no. 10
costObserved{10}	=0.009795308854495269;
costTotal{10}	=0.009795308854495269;
% SimulationKwadraticCostFunction: evaluation 10 : cost = 9.795E-3
%      cost =0.009795308854495269
% number of stored parameter sets changed from 8 to 9
% stop criterium1: imain > maxit 6<12
% stop criterium2: diff > abstol 0.10386024785543535<0.0020
% stop criterium3: diff ; reltol 0.10386024785543535<9.795308854495269E-5
% Application starting next step
%  START Sparse DuD outer iteration... 
%  START 2Sparse DuD outer iteration... true
% ======================================================
% Sparse DUD outer iteration no.7
% ======================================================
% -----------------------------------------------------
costs{7}	=[0.009795308854495269,0.11365555670993062,0.3069908765780312,0.49721719995488217,0.8023730416306033,4.677878580918254,9.740390131897941,10.475393981479684,15.753588696567682,16.54435532969439];
% -----------------------------------------------------
% dimensions: 
% N (number of tries (excluding initial try): 9
% Npar (number of parameters): 9
% Nobs (number of predictions=number of observations): 808
% RHS norm: 0.13996648780686946
% size dP_matrix 9 3.
% size xvals should be 3,202
% size xvals 3 202.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size A 808 9.
% Start search until improvement,
% norm delta_p: 8.762507798225127E-4
% Next try p=
%   Params - containing:
%      A-411= 0.03
%      A-412= -0.03
%      A-413= 0.02
%      A-414= 0.04
%      A-415= -0.03
%      A-416= -0.02
%      A-417= -2.369E-4
%      A-418= 9.719E-3
%      A-419= 0.02
%   delta_p=    [-6.514E-4,2.319E-4,-1.449E-4,-2.602E-4,5.457E-4,2.608E-4,7.197E-4,8.763E-4,5.265E-4]
% ========================================================================
% prepare no 11
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{11}	=[0.030151229158939793, -0.030006268348111913, 0.019981861627312015, 0.040072157574619585, -0.029970282064816663, -0.01999608517501405, -2.3685985104918107E-4, 0.009719413582519367, 0.019772692060601265];
% ========================================================================
% evaluate no. 11
costObserved{11}	=6.054494549112313E-4;
costTotal{11}	=6.054494549112313E-4;
% SimulationKwadraticCostFunction: evaluation 11 : cost = 6.054E-4
%      cost =6.054494549112313E-4
% number of stored parameter sets changed from 9 to 9
% stop criterium1: imain > maxit 7<12
% stop criterium2: diff > abstol 0.009189859399584038<0.0020
% stop criterium3: diff ; reltol 0.009189859399584038<6.054494549112313E-6
% Application starting next step
%  START Sparse DuD outer iteration... 
%  START 2Sparse DuD outer iteration... true
% ======================================================
% Sparse DUD outer iteration no.8
% ======================================================
% -----------------------------------------------------
costs{8}	=[6.054494549112313E-4,0.009795308854495269,0.11365555670993062,0.3069908765780312,0.49721719995488217,0.8023730416306033,4.677878580918254,9.740390131897941,10.475393981479684,15.753588696567682];
% -----------------------------------------------------
% dimensions: 
% N (number of tries (excluding initial try): 9
% Npar (number of parameters): 9
% Nobs (number of predictions=number of observations): 808
% RHS norm: 0.03479797278323067
% size dP_matrix 9 3.
% size xvals should be 3,202
% size xvals 3 202.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size dP_matrix 9 3.
% size xvals should be 3,101
% size xvals 3 101.
% size A 808 9.
% Start search until improvement,
% norm delta_p: 3.0249696274807264E-4
% Next try p=
%   Params - containing:
%      A-411= 0.03
%      A-412= -0.03
%      A-413= 0.02
%      A-414= 0.04
%      A-415= -0.03
%      A-416= -0.02
%      A-417= -5.805E-5
%      A-418= 0.01
%      A-419= 0.02
%   delta_p=    [-1.148E-4,-2.52E-6,1.56E-5,-7.111E-5,2.295E-5,-6.543E-5,1.788E-4,3.025E-4,2.255E-4]
% ========================================================================
% prepare no 12
%  Params: A-411, A-412, A-413, A-414, A-415, A-416, A-417, A-418, A-419
evaluatedParameters{12}	=[0.03003639882987584, -0.030008788844584803, 0.019997466314426886, 0.04000104798905921, -0.029947337013513965, -0.020061512573449278, -5.804883672604766E-5, 0.01002191054526744, 0.019998151574470786];
% ========================================================================
% evaluate no. 12
costObserved{12}	=2.6590129284645033E-5;
costTotal{12}	=2.6590129284645033E-5;
% SimulationKwadraticCostFunction: evaluation 12 : cost = 2.659E-5
%      cost =2.6590129284645033E-5
% number of stored parameter sets changed from 9 to 9
% stop criterium1: imain > maxit 8<12
% stop criterium2: diff > abstol 5.788593256265863E-4<0.0020
% stop criterium3: diff ; reltol 5.788593256265863E-4<2.6590129284645034E-7
% ===================================================================
% SimulationKwadraticCostfunction: optimal results
%     number of evaluations: 12
%     all cost values:
%         [10.475,16.544,9.74,15.754,4.678,0.802,0.497,0.307,0.114,9.795E-3,6.054E-4,2.659E-5]
%     all parameter values
%         [0,0.023,-0.017,2.696E-3,-8.834E-3,0.018,0.031,0.033,0.032,0.031,0.03,0.03;0,-4.161E-3,-6.261E-3,0.021,-0.043,-0.028,-0.032,-0.032,-0.031,-0.03,-0.03,-0.03;0,9.989E-3,-2.458E-3,-7.514E-3,1.039E-3,7.06E-3,0.013,0.016,0.019,0.02,0.02,0.02;0,-0.014,1.94E-3,0.011,0.02,0.034,0.034,0.036,0.039,0.04,0.04,0.04;0,0.011,1.849E-3,-0.012,-0.019,-0.04,-0.04,-0.038,-0.035,-0.031,-0.03,-0.03;0,-0.014,9.558E-4,7.113E-3,-0.012,-0.022,-0.022,-0.022,-0.023,-0.02,-0.02,-0.02;0,7.054E-3,6.161E-3,-0.013,2.619E-3,-7.143E-3,-4.9E-3,-2.944E-3,-1.315E-3,-9.565E-4,-2.369E-4,-5.805E-5;0,-0.012,-4.007E-3,0.011,9.611E-3,0.012,7.745E-3,7.425E-3,7.863E-3,8.843E-3,9.719E-3,0.01;0,-3.23E-3,0.016,-0.018,5.227E-3,0.018,0.018,0.018,0.018,0.019,0.02,0.02]
%     number of observations: 808
%     best cost:
%         cost = 2.659E-5
%     best parameters:
%         
%        Params - containing:
%           A-411= 0.03
%           A-412= -0.03
%           A-413= 0.02
%           A-414= 0.04
%           A-415= -0.03
%           A-416= -0.02
%           A-417= -5.805E-5
%           A-418= 0.01
%           A-419= 0.02
% ===================================================================
% Application Done
