What Is Optimization Toolbox?
What Is Optimization Toolbox? What Is Optimization Toolbox?
fminconIftheHessianmatrixcanalsobecomputedand the Hessian option is 'on',i.e., options = optimset('Hessian','on'), then the function fun mustreturn the Hessian value H, a symmetric matrix, at x in a third outputargument. Note that by checking the value of nargout you can avoidcomputing H when fun is called with only one or two output arguments (inthe case where the optimization algorithm only needs the values of f andg but not H).function [f,g,H] = myfun(x)f = ... % Compute the objective function value at xif nargout > 1 % fun called with two output argumentsg = ... % Gradient of the function evaluated at xif nargout > 2H = ... % Hessian evaluated at xendendThe Hessian matrix is the second partial derivatives matrix of f at thepoint x. Thatis,the(i,j)th component of H is the second partial derivativeof f with respect to x iand x j,a symmetric matrix.. The Hessian is by definitionnonlcon The function that computes the nonlinear inequality constraints c(x)
fminconthen the function nonlcon must also return, in the third and fourth outputarguments, GC, the gradient of c(x), andGCeq, the gradient of ceq(x). Notethat by checking the value of nargout the function can avoid computing GCand GCeq when nonlcon is called with only two output arguments (in thecase where the optimization algorithm only needs the values of c and ceqbut not GC and GCeq).Note Because the functions in Optimization Toolbox only accept inputs oftype double, user-supplied objective and nonlinear constraint functionsmust return outputs of type double.“Avoiding Global Variables via Anonymous and Nested Functions” onpage 2-20 explains how to parameterize the nonlinear constraint functionnonlcon, if necessary.function [c,ceq,GC,GCeq] = mycon(x)c = ...% Nonlinear inequalities at xceq = ... % Nonlinear equalities at xif nargout > 2 % nonlcon called with 4 outputsGC = ... % Gradients of the inequalitiesGCeq = ... % Gradients of the equalitiesendIf nonlcon returns a vector c of m components and x has length n, wherenis the length of x0, then the gradient GC of c(x) is an n-by-m matrix, whereGC(i,j) is the partial derivative of c(j) with respect to x(i) (i.e., the jthcolumn of GC is the gradient of the jth inequality constraint c(j)). Likewise,if ceq has p components, the gradient GCeq of ceq(x) is an n-by-p matrix,where GCeq(i,j) is the partial derivative of ceq(j) with respect to x(i) (i.e.,the jth column of GCeq is the gradient of the jth equality constraint ceq(j)).options“Options” on page 8-42 provides the function-specific details for the optionsvalues.8-39
- Page 271 and 272: Optimization Optionsspecifies Outpu
- Page 273 and 274: Optimization OptionsoptimValues Fie
- Page 275 and 276: Optimization OptionsoptimValues Fie
- 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: fminconfunThe function to be minimi
- Page 325 and 326: fmincongradhessianlambdaoutputGradi
- Page 327 and 328: fminconthe values of these fields i
- Page 329 and 330: fminconHessianHessMultIf 'on', fmin
- 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
fminconthen the function nonlcon must also return, in the third and fourth outputarguments, GC, the gradient of c(x), andGCeq, the gradient of ceq(x). Notethat by checking the value of nargout the function can avoid computing GCand GCeq when nonlcon is called with only two output arguments (in thecase where the optimization algorithm only needs the values of c and ceqbut not GC and GCeq).Note Because the functions in <strong>Optimization</strong> <strong>Toolbox</strong> only accept inputs oftype double, user-supplied objective and nonlinear constraint functionsmust return outputs of type double.“Avoiding Global Variables via Anonymous and Nested Functions” onpage 2-20 explains how to parameterize the nonlinear constraint functionnonlcon, if necessary.function [c,ceq,GC,GCeq] = mycon(x)c = ...% Nonlinear inequalities at xceq = ... % Nonlinear equalities at xif nargout > 2 % nonlcon called with 4 outputsGC = ... % Gradients of the inequalitiesGCeq = ... % Gradients of the equalitiesendIf nonlcon returns a vector c of m components and x has length n, wherenis the length of x0, then the gradient GC of c(x) is an n-by-m matrix, whereGC(i,j) is the partial derivative of c(j) with respect to x(i) (i.e., the jthcolumn of GC is the gradient of the jth inequality constraint c(j)). Likewise,if ceq has p components, the gradient GCeq of ceq(x) is an n-by-p matrix,where GCeq(i,j) is the partial derivative of ceq(j) with respect to x(i) (i.e.,the jth column of GCeq is the gradient of the jth equality constraint ceq(j)).options“Options” on page 8-42 provides the function-specific details for the optionsvalues.8-39