What Is Optimization Toolbox?

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2 TutorialThe last value shown in the MAX{F,constraints} column of the output showsthat the maximum value for all the time steps is 0.9996. The closed loopresponse with this result is shown in the following Closed-Loop ResponseUsing fminimax on page 2-36.This solution differs from the solution lsqnonlin because you are solvingdifferent problem formulations.Closed-Loop Response Using fminimaxSignal Processing ExampleConsider designing a linear-phase Finite Impulse Response (FIR) filter. Theproblem is to design a lowpass filter with magnitude one at all frequenciesbetween 0 and 0.1 Hz and magnitude zero between 0.15 and 0.5 Hz.The frequency response H(f) for such a filter is defined by2-36
Examples That Use Standard Algorithms(2-6)where A(f) isthe magnitude of the frequency response. One solution is toapply a goal attainment method to the magnitude of the frequency response.Given a function that computes the magnitude, the function fgoalattainwill attempt to varythemagnitudecoefficientsa(n) until the magnituderesponse matches the desired response within some tolerance. The functionthat computes the magnitude response is given in filtmin.m. This functiontakes a, themagnitude function coefficients, and w, the discretization of thefrequency domain we are interested in.To set up a goal attainment problem, you must specify the goal and weightsfor the problem. For frequencies between 0 and 0.1, the goal is one. Forfrequencies between 0.15 and 0.5, the goal is zero. Frequencies between 0.1and 0.15 are not specified, so no goals or weights are needed in this range.This information is stored in the variable goal passed to fgoalattain.The length of goal is the same as the length returned by the functionfiltmin. So that the goals are equally satisfied, usually weight wouldbe set to abs(goal). However, since some of the goals are zero, the effectof using weight=abs(goal) will force the objectives with weight 0tobesatisfied as hard constraints, and the objectives with weight 1 possibly to beunderattained (see “Goal Attainment Method” on page 3-49). Because all thegoals are close in magnitude, using a weight of unity for all goals will givethem equal priority. (Using abs(goal) for the weights is more importantwhen the magnitude of goal differs more significantly.) Also, settingoptions = optimset('GoalsExactAchieve',length(goal));specifies that each objective should be as nearaspossibletoitsgoalvalue(neither greater nor less than).2-37
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Optimization Toolbox 3User’s Guid
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Revision HistoryNovember 1990 First
Page 6 and 7: Acknowledgments
Page 8 and 9: Nonlinear Equations with Finite-Dif
Page 10 and 11: Quadratic Programming (QP) Subprobl
Page 12 and 13: Specifying the Options ............
Page 14 and 15: xivContents
Page 16 and 17: 1 Getting StartedWhat Is Optimizati
Page 18 and 19: 1 Getting StartedOptimization Examp
Page 20 and 21: 1 Getting Started[x, fval] =lsqlin(
Page 22 and 23: 2 TutorialLarge-Scale Examples (p.
Page 24 and 25: 2 TutorialMinimization (Continued)T
Page 26 and 27: 2 TutorialUsing the Optimization Fu
Page 28 and 29: 2 TutorialA choice of line search s
Page 30 and 31: 2 TutorialThe tutorial uses the fun
Page 32 and 33: 2 Tutorialfunction evaluations. See
Page 34 and 35: 2 TutorialTo restrict x inEquation2
Page 36 and 37: 2 Tutorialceq=[];DCeq = [ ];G conta
Page 38 and 39: 2 TutorialEquality Constrained Exam
Page 40 and 41: 2 Tutorialfunction y = findzero(b,
Page 42 and 43: 2 Tutorial3.7081Sharing Variables U
Page 44 and 45: 2 Tutorialcomponents.');end% Evalua
Page 46 and 47: 2 TutorialThe example produces the
Page 48 and 49: 2 TutorialClosed-Loop ResponseThe p
Page 50 and 51: 2 Tutorialfunction [Kp,Ki,Kd] = run
Page 52 and 53: 2 TutorialThe resulting closed-loop
Page 54 and 55: 2 Tutorialcalling the simulation tw
Page 58 and 59: 2 TutorialStep 1: Write an M-file f
Page 60 and 61: 2 TutorialLarge-Scale Examples•
Page 62 and 63: 2 TutorialNote The following table
Page 64 and 65: 2 TutorialLarge-Scale Problem Cover
Page 66 and 67: 2 Tutorialoptimset('Display','iter'
Page 68 and 69: 2 Tutorialeither) then, in this pro
Page 70 and 71: 2 TutorialNonlinear Least-Squares w
Page 72: 2 TutorialThe problem is to find x
Page 75 and 76: Large-Scale Examplesto zero (for fm
Page 77 and 78: Large-Scale Examples024681012141618
Page 79 and 80: Large-Scale Examplesfval =270.4790o
Page 81 and 82: Large-Scale Examplesans =1.1885e-01
Page 83 and 84: Large-Scale ExamplesW = Hinfo*Y - V
Page 85 and 86: Large-Scale Exampleswere not the sa
Page 87 and 88: Large-Scale Examplestradeoff is ben
Page 89 and 90: Large-Scale Examplesfunction W = qp
Page 91 and 92: Large-Scale Examples% RUNQPBOX4PREC
Page 93 and 94: Large-Scale Examplesalgorithm: 'lar
Page 95 and 96: Large-Scale Examplescgiterations: 0
Page 97 and 98: Large-Scale Examplesdoes not give a
Page 99 and 100: Default Options SettingsDetermining
Page 101 and 102: Displaying Iterative OutputDisplayi
Page 103 and 104: Displaying Iterative Outputbintprog
Page 105 and 106: Displaying Iterative OutputfsolveTh
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Displaying Iterative Outputlsqnonli
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Calling an Output Function Iterativ
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Optimizing Anonymous Functions Inst
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Optimizing Anonymous Functions Inst
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Typical Problems and How to Deal wi
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Typical Problems and How to Deal wi
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3Standard AlgorithmsStandard Algori
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Multiobjective Optimization (p. 3-4
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Demos of Medium-Scale MethodsDemos
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Unconstrained OptimizationFigure 3-
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Unconstrained Optimizationvariables
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Quasi-Newton ImplementationQuasi-Ne
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Quasi-Newton Implementationvalues o
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Quasi-Newton ImplementationMixed Cu
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Quasi-Newton ImplementationCase 4.3
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Least-Squares OptimizationIn proble
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Least-Squares OptimizationLevenberg
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Least-Squares OptimizationThe linea
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Nonlinear Systems of EquationsNonli
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Nonlinear Systems of Equationsand s
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Constrained OptimizationConstrained
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Constrained OptimizationGiven the p
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Constrained OptimizationUpdating th
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Constrained Optimizationis updated
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Constrained OptimizationInitializat
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Constrained OptimizationSimplex Alg
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Constrained Optimization3 Updates t
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Multiobjective OptimizationMultiobj
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Multiobjective OptimizationIn the t
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Multiobjective OptimizationThe afor
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Multiobjective OptimizationWhat is
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Multiobjective Optimization(3-51)wh
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Selected BibliographySelected Bibli
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Selected Bibliography[24] Han, S.P.
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4Large-Scale AlgorithmsLarge-Scale
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Trust-Region Methods for Nonlinear
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Trust-Region Methods for Nonlinear
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Preconditioned Conjugate GradientsP
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Linearly Constrained ProblemsLinear
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Linearly Constrained ProblemsThe sc
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Quadratic ProgrammingQuadratic Prog
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Large-Scale Linear ProgrammingLarge
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Large-Scale Linear ProgrammingThe a
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Large-Scale Linear ProgrammingWhile
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Selected Bibliography[11] Zhang, Y.
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5Optimization ToolGetting Started w
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Getting Started with the Optimizati
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Selecting a SolverSelecting a Solve
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Defining the ProblemDefining the Pr
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Defining the ProblemFunction to Min
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Defining the ProblemConstraintsLine
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Defining the ProblemM-file, or as a
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Defining the ProblemBounds are lowe
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Defining the Problemfseminf Problem
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Defining the ProblemLinear System o
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Defining the ProblemConstraintsBoun
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Defining the ProblemConstraintsWith
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Running a Problem in the Optimizati
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Specifying the Options• Xtoleranc
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Specifying the Optionsoption to a d
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Specifying the OptionsThe following
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Specifying the OptionsMultiobjectiv
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Specifying the OptionsPlot Function
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Specifying the Options• final —
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Importing and Exporting Your WorkIm
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Importing and Exporting Your WorkYo
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Optimization Tool ExamplesOptimizat
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Optimization Tool Examples6 In the
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Optimization Tool Examplesmax Line
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Optimization Tool ExamplesThe Aeq a
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6Argument and OptionsReferenceThis
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Function ArgumentsInput Arguments (
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Function ArgumentsInput Arguments (
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Function ArgumentsOutput Arguments
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Optimization OptionsOptimization Op
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Optimization Optionsspecifies Outpu
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Optimization OptionsoptimValues Fie
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Optimization OptionsStopping an Opt
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7Functions — By CategoryMinimizat
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Least Squares (Curve Fitting)Least
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Functions — AlphabeticalList8
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intprogx = bintprog(f,A,b,Aeq,Beq,x
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intprogBranchStrategyStrategy the a
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intprog• Verifies that no better
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intprogExampleTo minimize the funct
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colorPurposeSyntaxDescriptionColumn
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fgoalattainx = fgoalattain(fun,x0,g
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fgoalattainfunThefunctiontobeminimi
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fgoalattainfunction [c,ceq,GC,GCeq]
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fgoalattainattainfactorexitflaglamb
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fgoalattainFunValCheckGoalsExactAch
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fgoalattainExamplesConsider a linea
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fgoalattainof overattainment is met
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fgoalattainLimitationsReferencesThe
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fminbndInputArguments“Function Ar
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fminbndPlotFcnsPlots various measur
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fminbndLimitationsReferencesThe fun
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fminconx = fmincon(fun,x0,A,b) star
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fminconfunThe function to be minimi
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fminconthen the function nonlcon mu
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fmincongradhessianlambdaoutputGradi
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fminconthe values of these fields i
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fminconHessianHessMultIf 'on', fmin
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fminconPrecondBandWidth Upper bandw
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fminconSince both constraints are l
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fmincon• A dense (or fairly dense
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fminconReferences[1] Coleman, T.F.
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fminimaxx = fminimax(fun,x,A,b,Aeq,
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fminimaxfunThe function to be minim
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fminimaxIf nonlcon returns a vector
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fminimaxlambdamaxfvaloutputStructur
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fminimaxMeritFunctionMinAbsMaxOutpu
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fminimaxx0 = [0.1; 0.1]; % Make a s
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fminimax[3] Han, S.P., “A Globall
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fminsearchInputArguments“Function
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fminsearchOutputFcnPlotFcnsTolFunSp
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fminsearcha = sqrt(2);banana = @(x)
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fminuncPurposeEquationFind minimum
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fminuncfunThefunctiontobeminimized.
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fminuncexitflaggradhessianoutputInt
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fminuncLarge-Scale and Medium-Scale
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fminuncHessianHessMultIf 'on', fmin
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fminuncPrecondBandWidthTolPCGUpper
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fminuncx0 = [1,1];[x,fval] = fminun
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fminunc“Trust-Region Methods for
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fseminfPurposeEquationFind minimum
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fseminf“Avoiding Global Variables
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fseminfoptions“Options” on page
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fseminflambdaoutput5 Magnitude of d
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fseminfOutputFcnPlotFcnsRelLineSrch
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fseminfSecond, write an M-file, myc
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fseminfThe plot command inside 'myc
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fseminfThe goal was to minimize the
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fsolvePurposeEquationSolve system o
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fsolvefunThe nonlinear system of eq
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fsolvefuncCountalgorithmcgiteration
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fsolvePlotFcnsTolFunPlots various m
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fsolveJacobPatternMaxPCGIterPrecond
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fsolve[x,fval] = fsolve(@myfun,x0,o
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fsolveYoucanformulateandsolvethepro
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fsolveLimitationsThe function to be
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fzeroPurposeSyntaxDescriptionFind r
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fzeroDisplayFunValCheckOutputFcnLev
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fzerowrite an M-file called f.m.fun
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fzmultPurposeSyntaxMultiplication w
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linprogPurposeEquationSolve linear
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linproglambdaoutput-2 No feasible p
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linprogsubject toFirst, enter the c
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linprogDiagnosticsLarge-Scale Optim
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linprogthe primal objective < -1e+1
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lsqcurvefitPurposeEquationSolve non
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lsqcurvefitfunThe function you want
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lsqcurvefitoutputupperUpper bounds
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lsqcurvefitJacobianMaxFunEvalsMaxIt
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lsqcurvefitJacobPatternMaxPCGIterSp
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lsqcurvefitNote that at the time th
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lsqcurvefitof J with many nonzeros,
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lsqlinPurposeEquationSolve constrai
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lsqlinlambdaoutput3 Change in the r
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lsqlinDiagnosticsDisplayMaxIterTypi
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lsqlinPrecondBandWidthUpper bandwid
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lsqlinNotesFor problems with no con
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lsqlinReferences[1] Coleman, T.F. a
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lsqnonlinreturn a vector of values
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lsqnonlinOutputArguments“Function
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lsqnonlinalgorithm. See “Optimiza
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lsqnonlinJacobMultFunction handle f
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lsqnonlinfor(that is, F should have
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lsqnonlinand Requirements on page 2
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lsqnonnegPurposeEquationSolve nonne
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lsqnonneglambdaoutputVector contain
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optimgetPurposeSyntaxDescriptionExa
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optimsetIn the following lists, val
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optimsetLineSearchType'cubicpoly' |
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optimtoolPurposeSyntaxDescriptionTo
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quadprogPurposeEquationSolve quadra
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quadproglambdaoutput3 Change in the
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quadprogLargeScaleUse large-scale a
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quadprogTolPCGTermination tolerance
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quadprogNotesIn general quadprog lo
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quadprogWhen the equality constrain
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IndexIndex ε-Constraint method 3-4
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Indexinfeasible solution warninglin
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Indexdescriptions 6-8possible value
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