What Is Optimization Toolbox?
What Is Optimization Toolbox? What Is Optimization Toolbox?
lsqnonneg[x,resnorm,residual,exitflag,output] = lsqnonneg(...) returnsa structure output that contains information about the optimization.[x,resnorm,residual,exitflag,output,lambda] =lsqnonneg(...) returns the Lagrange multipliers in the vector lambda.InputArguments“Function Arguments” on page 6-2 contains general descriptionsof arguments passed into lsqnonneg. This section providesfunction-specific details for options:optionsUse optimset to set or change the values of thesefields in the options structure, options. See“Optimization Options” on page 6-8 for detailedinformation.DisplayLevel of display. 'off' displays nooutput; 'final' displays just the finaloutput; 'notify' (default) displaysoutput only if the function does notconverge.TolX Termination tolerance on x.OutputArguments“Function Arguments” on page 6-2 contains general descriptionsof arguments returned by lsqnonneg. This section providesfunction-specific details for exitflag, lambda, andoutput:exitflagInteger identifying the reasonthealgorithmterminated. The following lists the values ofexitflag and the corresponding reasons thealgorithm terminated.1 Function converged to asolution x.0 Number of iterations exceededoptions.MaxIter.8-180
lsqnonneglambdaoutputVector containing the Lagrange multipliers:lambda(i)0.Structure containing information about theoptimization. The fields areiterationsalgorithmNumber of iterations takenAlgorithm usedExamplesCompare the unconstrained least-squares solution to the lsqnonnegsolution for a 4-by-2 problem.C = [0.0372 0.28690.6861 0.70710.6233 0.62450.6344 0.6170];d = [0.85870.17810.07470.8405];[C\d, lsqnonneg(C,d)] =-2.5627 03.1108 0.6929[norm(C*(C\d)-d), norm(C*lsqnonneg(C,d)-d)] =0.6674 0.9118The solution from lsqnonneg does not fit as well as the least-squaressolution. However, the nonnegative least-squares solution has nonegative components.8-181
- Page 413 and 414: fzmultPurposeSyntaxMultiplication w
- Page 415 and 416: linprogPurposeEquationSolve linear
- Page 417 and 418: linproglambdaoutput-2 No feasible p
- Page 419 and 420: linprogsubject toFirst, enter the c
- Page 421 and 422: linprogDiagnosticsLarge-Scale Optim
- Page 423 and 424: linprogthe primal objective < -1e+1
- Page 425 and 426: lsqcurvefitPurposeEquationSolve non
- Page 427 and 428: lsqcurvefitfunThe function you want
- Page 429 and 430: lsqcurvefitoutputupperUpper bounds
- Page 431 and 432: lsqcurvefitJacobianMaxFunEvalsMaxIt
- Page 433 and 434: lsqcurvefitJacobPatternMaxPCGIterSp
- Page 435 and 436: lsqcurvefitNote that at the time th
- Page 437 and 438: lsqcurvefitof J with many nonzeros,
- Page 439 and 440: lsqlinPurposeEquationSolve constrai
- Page 441 and 442: lsqlinlambdaoutput3 Change in the r
- Page 443 and 444: lsqlinDiagnosticsDisplayMaxIterTypi
- Page 445 and 446: lsqlinPrecondBandWidthUpper bandwid
- Page 447 and 448: lsqlinNotesFor problems with no con
- Page 449 and 450: lsqlinReferences[1] Coleman, T.F. a
- Page 451 and 452: lsqnonlinreturn a vector of values
- Page 453 and 454: lsqnonlinOutputArguments“Function
- Page 455 and 456: lsqnonlinalgorithm. See “Optimiza
- Page 457 and 458: lsqnonlinJacobMultFunction handle f
- Page 459 and 460: lsqnonlinfor(that is, F should have
- Page 461 and 462: lsqnonlinand Requirements on page 2
- Page 463: lsqnonnegPurposeEquationSolve nonne
- Page 467 and 468: optimgetPurposeSyntaxDescriptionExa
- Page 469 and 470: optimsetIn the following lists, val
- Page 471 and 472: optimsetLineSearchType'cubicpoly' |
- Page 473 and 474: optimtoolPurposeSyntaxDescriptionTo
- Page 475 and 476: quadprogPurposeEquationSolve quadra
- Page 477 and 478: quadproglambdaoutput3 Change in the
- Page 479 and 480: quadprogLargeScaleUse large-scale a
- Page 481 and 482: quadprogTolPCGTermination tolerance
- Page 483 and 484: quadprogNotesIn general quadprog lo
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- Page 487 and 488: IndexIndex ε-Constraint method 3-4
- Page 489 and 490: Indexinfeasible solution warninglin
- Page 491 and 492: Indexdescriptions 6-8possible value
lsqnonneglambdaoutputVector containing the Lagrange multipliers:lambda(i)0.Structure containing information about theoptimization. The fields areiterationsalgorithmNumber of iterations takenAlgorithm usedExamplesCompare the unconstrained least-squares solution to the lsqnonnegsolution for a 4-by-2 problem.C = [0.0372 0.28690.6861 0.70710.6233 0.62450.6344 0.6170];d = [0.85870.17810.07470.8405];[C\d, lsqnonneg(C,d)] =-2.5627 03.1108 0.6929[norm(C*(C\d)-d), norm(C*lsqnonneg(C,d)-d)] =0.6674 0.9118The solution from lsqnonneg does not fit as well as the least-squaressolution. However, the nonnegative least-squares solution has nonegative components.8-181
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