DOTcvpSB: a Matlab Toolbox for Dynamic Optimization in Systems ...

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DOTcvpSB: a Matlab Toolbox for Dynamic Optimization in Systems Biologydata.options.profiler test the performance of the toolbox string [’on’|’off’] ’off’data.options.multistart set 1 if the multistart is off, otherwiseinteger [positive] – 1you have to put here some in-teger valuedata.options.action what will be done string [’single-optimization’ ’single-optimization’|’re-optimization’|’hybrid-strategy’|’simulation’]11.2 DEFAULT SETTINGS OF THE SUCESSIVE RE-OPTIMIZATIONDOTcvp: dotcvp_reoptimization_default.m.m1 function [data] = dotcvp_reoptimization_default(data)23 data.option.reoptimization.NRO = 4; % compute the number of refining optimizations4 data.option.reoptimization.Increasing = 2; % how quickly will the mesh refinement be increased5 data.option.reoptimization.NLPtol = 1e-003; % initial NLP tolerance level6 data.option.reoptimization.IVPRelTol = 1e-005; % initial IVP relative tolerance level7 data.option.reoptimization.IVPAbsTol = 1e-005; % initial IVP absolute tolerance level8 data.option.reoptimization.SensAbsTol = 1e-005; % initial absolute tolerance for sensitivity variables910 % Note that values of NLP and IVP tolerances will be increased with the11 % mesh refinement algorithm until they reach the final tolerances defined12 % by the user in the initial file.1314 end11.3 DEFAULT SETTINGS OF THE HYBRID STRATEGYDOTcvp: dotcvp_hybrid_strategy_default.m.m1 function [data] = dotcvp_hybrid_strategy_default(data)23 switch data.option.HybridStrategy.method4 case{'stochastic'}5 % first step [a stochastic solver]: please fill basic settings for6 % a stochastic solver, otherwise they will be taken from the user7 % input file89 data.option.HybridStrategy.IVPRelTol = 1e-005; % IVP relative tolerance level10 data.option.HybridStrategy.IVPAbsTol = 1e-005; % IVP absolute tolerance level11 data.option.HybridStrategy.NLPTol = 1e-003; % NLP tolerance level12 data.option.HybridStrategy.NLPSolver = 'DE'; % chose a solver ['FMINCON'|'IPOPT'|'SRES'|'DE'|'ACOMI'|'MISQP'|'MITS']13 data.option.HybridStrategy.NLPsettings = 'None'; % insert the file name that contains settings for NLP solver, otherwise 'None'14 data.option.HybridStrategy.MaxIter = 50; % maximum number of iterations15 data.option.HybridStrategy.MaxCPUTime = 60*10; % maximum CPU time of the optimization (60*60*0.25) = 15 minutes16 data.option.HybridStrategy.intermediate = 'off'; % ['on'|'off'|'silent'] display of the intermediate results1718 case{'deterministic'}19 % second step [a deterministic solver]: all settings for a20 % deterministic solver are taken from the user input file, but the21 % user can introduce some new or special settings, which rewrites22 % those from the input file. The name of the structure is the same23 % as the name in the user input file, e.g. data.nlp.RHO, etc.2425 end26 end11.4 DEFAULT SETTINGS OF THE SIMULATIONDOTcvp: dotcvp_simulation_default.m.m1 function [data] = dotcvp_simulation_default(data)23 data.options.simulation.IVPsolver = 'ode15s'; % ['ode45'|'ode23'|'ode23t'|'ode23tb'|'ode113'|'ode15s'|'ode23s']4 data.options.simulation.MaxStep = 1e-1; % ['positive scalar'] step size5 data.options.simulation.grid = 'yes'; % ['yes'|'no'] display of the grid in the figure(s)6 data.options.simulation.legend = 'yes'; % ['yes'|'no'] display of the legend in the figure7 data.options.simulation.semilogx = 'no'; % ['yes'|'no'] if the logarithmic (base 10) scale is used for the X-axis8 data.options.simulation.output = 'one'; % ['one'|'all'] how many state figures will be depicted910 end11.5 DEFAULT SETTINGS OF NLP OR MINLP SOLVERSThe following are the default values for the different NLP solvers. The corresponding m-files can be editedby the user in order to change these defaults if needed.11.5.1 ACOmiDOTcvp: dotcvp_acomi_default.m1 function [data] = dotcvp_acomi_default(data)23 data.options.acomi.report = 0; % create a report file containing all solution information: [1/0] = [Yes/No]4 data.options.acomi.acc = 10^(-3); % accuracy for both: object-function & constraints5 data.options.acomi.maxeval = data.nlp.MaxIter; % maximum number of function evaluationsPage – 51
DOTcvpSB: a Matlab Toolbox for Dynamic Optimization in Systems Biology6 data.options.acomi.maxtime = data.nlp.MaxCPUTime; % maximum CPU time [sec]7 data.options.acomi.fex = -inf; % stopping criteria: if objective function = fex -> stop8 data.options.acomi.loc_solver = 1; % use local solver at within ACOMI: [1/0] = [Yes/No]9 data.options.acomi.loc_type = 'MISQP'; % select a local solver in ACOMI: ['MISQP']10 data.options.acomi.loc_start = 1; % 10 seconds multistart of the local solver before (!) ACOMI: [1/0] = [Yes/No]11 %data.options.acomi.oracle = -10^(12); % oracle parameter for penalty function [optional]; with constraints: 10^(12)1213 end11.5.2 DEDOTcvp: dotcvp_de_default.m1 function [data] = dotcvp_de_default(data)23 data.options.de.VTR = -Inf; % for minimization "Value To Reach" (stop when objective function < VTR)4 data.options.de.F = 0.7; % DE-stepsize F from interval [0, 2] (recommended 0.7/0.8)5 data.options.de.CR = 1.0; % crossover probability constant from interval [0, 1]6 data.options.de.strategy = 3; % recommended 3 or 87 data.options.de.max_time = data.nlp.MaxCPUTime; % maximum CPU time [sec]8 data.options.de.NLPtol = data.nlp.NLPtol; % NLP tolerance level9 data.options.de.itermax = data.nlp.MaxIter; % maximum number of iterations (generations)10 data.options.de.MaxFunEvals = inf; % maximum function evaluation11 data.options.de.intermediate = data.options.intermediate; % display of the intermediate results1213 end11.5.3 FMINCONDOTcvp: dotcvp_fmincon_default.m1 function [data] = dotcvp_fmincon_default(data)23 data.options.fmincon.MaxTime = data.nlp.MaxCPUTime; % maximum CPU time [sec]4 data.options.fmincon.GradObj = 'on'; % objective function gradients defined by the user5 data.options.fmincon.GradConstr = 'on'; % constraints gradients defined by the user6 data.options.fmincon.TolFun = data.nlp.NLPtol; % termination tolerance on the function value7 data.options.fmincon.TolCon = data.nlp.NLPtol; % termination tolerance on the constraint violation8 data.options.fmincon.TolX = data.nlp.NLPtol; % termination tolerance on the function value9 data.options.fmincon.Hessian = 'off'; % hessian information10 data.options.fmincon.DerivativeCheck = 'off'; % compare user-supplied derivatives to FD derivatives11 data.options.fmincon.MaxIter = data.nlp.MaxIter; % maximum number of iterations12 data.options.fmincon.MaxFunEvals = 10*data.nlp.MaxIter; % maximum number of function evaluations1314 end11.5.4 IPOPTDOTcvp: dotcvp_ipopt_default.m1 function [data] = dotcvp_ipopt_default(data)23 data.options.ipopt.jac_c_constant = 'no'; % indicates whether all equality constraints are linear ['yes','no']4 data.options.ipopt.hessian_approximation = 'limited-memory'; % indicates what Hessian information is to be used5 ['exact','limited-memory']6 data.options.ipopt.mu_strategy = 'adaptive'; % update strategy for barrier parameter ['adaptive','monotone']7 data.options.ipopt.nlp_scaling_method = 'gradient-based'; % select the technique used for scaling the NLP8 ['none','user-scaling','gradient-based','equilibration-based']9 data.options.ipopt.print_options_documentation = 'no'; % switch to print all algorithmic options ['yes','no']10 %data.options.ipopt.print_level = 5; % [print_level] Output verbosity level [2
Page 1 and 2: TECHNICAL REPORTDOTcvpSB: a Matlab
Page 3 and 4: Main Web PageIt is possible to get
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Page 13 and 14: CHAPTER 2Optimal control problem2.1
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Page 30 and 31: CHAPTER 7GUI for DOTcvp7.1 STEP BY
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Page 50 and 51: CHAPTER 11Default settingsThe user
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