What Is Optimization Toolbox?
What Is Optimization Toolbox? What Is Optimization Toolbox?
fminconGradObjMaxFunEvalsMaxIterOutputFcnPlotFcnsTolFunGradient for the objective function defined bythe user. See the preceding description of funto see how to define the gradient in fun. Youmust provide the gradient to use the large-scalemethod. It is optional for the medium-scalemethod.Maximum number of function evaluationsallowed.Maximum number of iterations allowed.Specify one or more user-defined functions thatan optimization function calls at each iteration.See “Output Function” on page 6-16.Plots various measures of progress while thealgorithm executes, select from predefined plotsor write your own. Specifying @optimplotx plotsthe current point; @optimplotfunccount plotsthe function count; @optimplotfval plots thefunction value; @optimplotconstrviolationplots the maximum constraint violation;@optimplotstepsize plots the step size;@optimplotfirstorderopt plots the first-orderof optimality.Termination tolerance on the function value.TolConTermination tolerance on the constraintviolation.TolX Termination tolerance on x.TypicalXTypical x values.Large-Scale Algorithm OnlyThese options are used only by the large-scale algorithm:8-44
fminconHessianHessMultIf 'on', fmincon uses a user-defined Hessian(defined in fun), or Hessian information (whenusing HessMult), for the objective function. If'off', fmincon approximates the Hessian usingfinite differences.Function handle for Hessian multiply function.For large-scale structured problems, this functioncomputes the Hessian matrix product H*Ywithout actually forming H. The function is ofthe formW = hmfun(Hinfo,Y,p1,p2,...)where Hinfo and possibly the additionalparameters p1,p2,... contain the matrices usedto compute H*Y.The first argument must be the same as the thirdargument returned by the objective function fun,for example by[f,g,Hinfo] = fun(x)8-45
- Page 277 and 278: Optimization OptionsoptimValues Fie
- Page 279 and 280: Optimization OptionsStopping an Opt
- Page 281 and 282: 7Functions — By CategoryMinimizat
- Page 283 and 284: Least Squares (Curve Fitting)Least
- Page 285 and 286: Functions — AlphabeticalList8
- Page 287 and 288: intprogx = bintprog(f,A,b,Aeq,Beq,x
- Page 289 and 290: intprogBranchStrategyStrategy the a
- Page 291 and 292: intprog• Verifies that no better
- Page 293 and 294: intprogExampleTo minimize the funct
- Page 295 and 296: colorPurposeSyntaxDescriptionColumn
- Page 297 and 298: fgoalattainx = fgoalattain(fun,x0,g
- Page 299 and 300: fgoalattainfunThefunctiontobeminimi
- Page 301 and 302: fgoalattainfunction [c,ceq,GC,GCeq]
- Page 303 and 304: fgoalattainattainfactorexitflaglamb
- Page 305 and 306: fgoalattainFunValCheckGoalsExactAch
- Page 307 and 308: fgoalattainExamplesConsider a linea
- Page 309 and 310: fgoalattainof overattainment is met
- Page 311 and 312: fgoalattainLimitationsReferencesThe
- Page 313 and 314: fminbndInputArguments“Function Ar
- Page 315 and 316: fminbndPlotFcnsPlots various measur
- Page 317 and 318: fminbndLimitationsReferencesThe fun
- Page 319 and 320: fminconx = fmincon(fun,x0,A,b) star
- Page 321 and 322: fminconfunThe function to be minimi
- Page 323 and 324: fminconthen the function nonlcon mu
- Page 325 and 326: fmincongradhessianlambdaoutputGradi
- Page 327: fminconthe values of these fields i
- Page 331 and 332: fminconPrecondBandWidth Upper bandw
- Page 333 and 334: fminconSince both constraints are l
- Page 335 and 336: fmincon• A dense (or fairly dense
- Page 337 and 338: fminconReferences[1] Coleman, T.F.
- Page 339 and 340: fminimaxx = fminimax(fun,x,A,b,Aeq,
- Page 341 and 342: fminimaxfunThe function to be minim
- Page 343 and 344: fminimaxIf nonlcon returns a vector
- Page 345 and 346: fminimaxlambdamaxfvaloutputStructur
- Page 347 and 348: fminimaxMeritFunctionMinAbsMaxOutpu
- Page 349 and 350: fminimaxx0 = [0.1; 0.1]; % Make a s
- Page 351 and 352: fminimax[3] Han, S.P., “A Globall
- Page 353 and 354: fminsearchInputArguments“Function
- Page 355 and 356: fminsearchOutputFcnPlotFcnsTolFunSp
- Page 357 and 358: fminsearcha = sqrt(2);banana = @(x)
- Page 359 and 360: fminuncPurposeEquationFind minimum
- Page 361 and 362: fminuncfunThefunctiontobeminimized.
- Page 363 and 364: fminuncexitflaggradhessianoutputInt
- Page 365 and 366: fminuncLarge-Scale and Medium-Scale
- Page 367 and 368: fminuncHessianHessMultIf 'on', fmin
- Page 369 and 370: fminuncPrecondBandWidthTolPCGUpper
- Page 371 and 372: fminuncx0 = [1,1];[x,fval] = fminun
- Page 373 and 374: fminunc“Trust-Region Methods for
- Page 375 and 376: fseminfPurposeEquationFind minimum
- Page 377 and 378: fseminf“Avoiding Global Variables
fminconGradObjMaxFunEvalsMaxIterOutputFcnPlotFcnsTolFunGradient for the objective function defined bythe user. See the preceding description of funto see how to define the gradient in fun. Youmust provide the gradient to use the large-scalemethod. It is optional for the medium-scalemethod.Maximum number of function evaluationsallowed.Maximum number of iterations allowed.Specify one or more user-defined functions thatan optimization function calls at each iteration.See “Output Function” on page 6-16.Plots various measures of progress while thealgorithm executes, select from predefined plotsor write your own. Specifying @optimplotx plotsthe current point; @optimplotfunccount plotsthe function count; @optimplotfval plots thefunction value; @optimplotconstrviolationplots the maximum constraint violation;@optimplotstepsize plots the step size;@optimplotfirstorderopt plots the first-orderof optimality.Termination tolerance on the function value.TolConTermination tolerance on the constraintviolation.TolX Termination tolerance on x.TypicalXTypical x values.Large-Scale Algorithm OnlyThese options are used only by the large-scale algorithm:8-44
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