What Is Optimization Toolbox?
What Is Optimization Toolbox? What Is Optimization Toolbox?
2 TutorialBecause the iterative display was set using optimset, theresultsdisplayedareResiduals: Primal Dual Upper Duality TotalInfeas Infeas Bounds Gap RelA*x-b A'*y+z-w-f {x}+s-ub x'*z+s'*w Error-------------------------------------------------------------Iter 0: 1.67e+003 8.11e+002 1.35e+003 5.30e+006 2.92e+001Iter 1: 1.37e+002 1.33e+002 1.11e+002 1.27e+006 2.48e+000Iter 2: 3.56e+001 2.38e+001 2.89e+001 3.42e+005 1.99e+000Iter 3: 4.86e+000 8.88e+000 3.94e+000 1.40e+005 1.89e+000Iter 4: 4.24e-001 5.89e-001 3.44e-001 1.91e+004 8.41e-001Iter 5: 1.23e-001 2.02e-001 9.97e-002 8.41e+003 5.79e-001Iter 6: 3.98e-002 7.91e-002 3.23e-002 4.05e+003 3.52e-001Iter 7: 7.25e-003 3.83e-002 5.88e-003 1.85e+003 1.85e-001Iter 8: 1.47e-003 1.34e-002 1.19e-003 8.12e+002 8.52e-002Iter 9: 2.52e-004 3.39e-003 2.04e-004 2.78e+002 2.99e-002Iter 10: 3.46e-005 1.08e-003 2.81e-005 1.09e+002 1.18e-002Iter 11: 6.97e-007 3.23e-012 5.64e-007 1.48e+001 1.62e-003Iter 12: 5.10e-007 3.25e-012 3.18e-008 8.32e-001 9.09e-005Iter 13: 5.06e-006 1.43e-010 3.86e-009 7.26e-002 7.94e-006Iter 14: 2.15e-006 2.17e-010 6.53e-012 1.11e-003 1.21e-007Iter 15: 7.34e-008 2.33e-009 3.38e-013 8.62e-008 1.29e-009Optimization terminated.You can see the returned values of exitflag, fval, andoutput:exitflag =1fval =9.1464e+003output =iterations: 15algorithm: 'large-scale: interior point'cgiterations: 208message: 'Optimization terminated.'This time the number of PCG iterations (in output.cgiterations) isnonzerobecause the dense columns in Aeq are detected. Instead of using a sparseCholesky factorization, linprog tries to use the Sherman-Morrison formula tosolve a linear system involving Aeq*Aeq'. If the Sherman-Morrison formula2-76
Large-Scale Examplesdoes not give a satisfactory residual, a PCG iteration is used. See “MainAlgorithm” on page 4-15.2-77
- 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 56 and 57: 2 TutorialThe last value shown in t
- 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: Large-Scale Examplescgiterations: 0
- 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
- Page 107 and 108: Displaying Iterative Outputlsqnonli
- Page 109 and 110: Calling an Output Function Iterativ
- Page 111 and 112: Calling an Output Function Iterativ
- Page 113 and 114: Calling an Output Function Iterativ
- Page 115 and 116: Optimizing Anonymous Functions Inst
- Page 117 and 118: Optimizing Anonymous Functions Inst
- Page 119 and 120: Typical Problems and How to Deal wi
- Page 121 and 122: Typical Problems and How to Deal wi
- Page 123 and 124: 3Standard AlgorithmsStandard Algori
- Page 125 and 126: Multiobjective Optimization (p. 3-4
- Page 127 and 128: Demos of Medium-Scale MethodsDemos
- Page 129 and 130: Unconstrained OptimizationFigure 3-
- Page 131 and 132: Unconstrained Optimizationvariables
- Page 133 and 134: Quasi-Newton ImplementationQuasi-Ne
- Page 135 and 136: Quasi-Newton Implementationvalues o
- Page 137 and 138: Quasi-Newton ImplementationMixed Cu
- Page 139 and 140: Quasi-Newton ImplementationCase 4.3
- Page 141 and 142: Least-Squares OptimizationIn proble
- Page 143 and 144: Least-Squares OptimizationLevenberg
- Page 145 and 146: Least-Squares OptimizationThe linea
2 TutorialBecause the iterative display was set using optimset, theresultsdisplayedareResiduals: Primal Dual Upper Duality TotalInfeas Infeas Bounds Gap RelA*x-b A'*y+z-w-f {x}+s-ub x'*z+s'*w Error-------------------------------------------------------------Iter 0: 1.67e+003 8.11e+002 1.35e+003 5.30e+006 2.92e+001Iter 1: 1.37e+002 1.33e+002 1.11e+002 1.27e+006 2.48e+000Iter 2: 3.56e+001 2.38e+001 2.89e+001 3.42e+005 1.99e+000Iter 3: 4.86e+000 8.88e+000 3.94e+000 1.40e+005 1.89e+000Iter 4: 4.24e-001 5.89e-001 3.44e-001 1.91e+004 8.41e-001Iter 5: 1.23e-001 2.02e-001 9.97e-002 8.41e+003 5.79e-001Iter 6: 3.98e-002 7.91e-002 3.23e-002 4.05e+003 3.52e-001Iter 7: 7.25e-003 3.83e-002 5.88e-003 1.85e+003 1.85e-001Iter 8: 1.47e-003 1.34e-002 1.19e-003 8.12e+002 8.52e-002Iter 9: 2.52e-004 3.39e-003 2.04e-004 2.78e+002 2.99e-002Iter 10: 3.46e-005 1.08e-003 2.81e-005 1.09e+002 1.18e-002Iter 11: 6.97e-007 3.23e-012 5.64e-007 1.48e+001 1.62e-003Iter 12: 5.10e-007 3.25e-012 3.18e-008 8.32e-001 9.09e-005Iter 13: 5.06e-006 1.43e-010 3.86e-009 7.26e-002 7.94e-006Iter 14: 2.15e-006 2.17e-010 6.53e-012 1.11e-003 1.21e-007Iter 15: 7.34e-008 2.33e-009 3.38e-013 8.62e-008 1.29e-009<strong>Optimization</strong> terminated.You can see the returned values of exitflag, fval, andoutput:exitflag =1fval =9.1464e+003output =iterations: 15algorithm: 'large-scale: interior point'cgiterations: 208message: '<strong>Optimization</strong> terminated.'This time the number of PCG iterations (in output.cgiterations) isnonzerobecause the dense columns in Aeq are detected. Instead of using a sparseCholesky factorization, linprog tries to use the Sherman-Morrison formula tosolve a linear system involving Aeq*Aeq'. If the Sherman-Morrison formula2-76