Engineering Design Optimization

640 Pages·2022·13.822 MB·English

by  Joaquim R. R. A. Martins, Andrew Ning

E
nginEEring
D O
Esign ptimizatiOn

Joaquim R. R. A. Martins
Andrew Ning
Engineering
Design
Optimization
joaquimr.r.a.martins
UniversityofMichigan
andrewning
BrighamYoungUniversity
Thisistheelectronicversionofthebook,whichisavailable
onthefollowingwebpage:
https://mdobook.github.io
Thepagenumbershavebeenadjustedtomatchthoseofthe
printedbook,whichisavailableat:
https://www.cambridge.org/us/academic/subjects/
engineering/control-systems-and-optimization/
engineering-design-optimization
Pleasecitethisbookas:
JoaquimR.R.A.MartinsandAndrewNing. EngineeringDe-
signOptimization. CambridgeUniversityPress,2021. ISBN:
9781108833417.
Copyright
© 2021 JoaquimR.R.A.MartinsandAndrewNing. Allrights
reserved.
Publication
Firstelectronicedition: January2020.
Contents
Contents v
Preface xi
Acknowledgements xiii
1 Introduction 1
1.1 DesignOptimizationProcess 2
1.2 OptimizationProblemFormulation 6
1.3 OptimizationProblemClassification 17
1.4 OptimizationAlgorithms 21
1.5 SelectinganOptimizationApproach 26
1.6 Notation 28
1.7 Summary 29
Problems 30
2 AShortHistoryofOptimization 33
2.1 TheFirstProblems: OptimizingLengthandArea 33
2.2 OptimizationRevolution: DerivativesandCalculus 34
2.3 TheBirthofOptimizationAlgorithms 36
2.4 TheLastDecades 39
2.5 TowardaDiverseFuture 43
2.6 Summary 45
3 NumericalModelsandSolvers 47
3.1 ModelDevelopmentforAnalysisversusOptimization 47
3.2 ModelingProcessandTypesofErrors 48
3.3 NumericalModelsasResidualEquations 50
3.4 DiscretizationofDifferentialEquations 52
3.5 NumericalErrors 53
3.6 OverviewofSolvers 61
3.7 RateofConvergence 63
3.8 Newton-BasedSolvers 66
3.9 ModelsandtheOptimizationProblem 70
v
Contents vi
3.10 Summary 73
Problems 75
4 UnconstrainedGradient-BasedOptimization 79
4.1 Fundamentals 80
4.2 TwoOverallApproachestoFindinganOptimum 94
4.3 LineSearch 96
4.4 SearchDirection 110
4.5 Trust-RegionMethods 139
4.6 Summary 147
Problems 149
5 ConstrainedGradient-BasedOptimization 153
5.1 ConstrainedProblemFormulation 154
5.2 Understandingn-DimensionalSpace 156
5.3 OptimalityConditions 158
5.4 PenaltyMethods 175
5.5 SequentialQuadraticProgramming 187
5.6 Interior-PointMethods 204
5.7 ConstraintAggregation 211
5.8 Summary 214
Problems 215
6 ComputingDerivatives 223
6.1 Derivatives,Gradients,andJacobians 223
6.2 OverviewofMethodsforComputingDerivatives 225
6.3 SymbolicDifferentiation 226
6.4 FiniteDifferences 227
6.5 ComplexStep 232
6.6 AlgorithmicDifferentiation 237
6.7 ImplicitAnalyticMethods—DirectandAdjoint 252
6.8 SparseJacobiansandGraphColoring 262
6.9 UnifiedDerivativesEquation 265
6.10 Summary 275
Problems 277
7 Gradient-FreeOptimization 281
7.1 WhentoUseGradient-FreeAlgorithms 281
7.2 ClassificationofGradient-FreeAlgorithms 284
7.3 Nelder–MeadAlgorithm 287
7.4 GeneralizedPatternSearch 292
7.5 DIRECTAlgorithm 298
7.6 GeneticAlgorithms 306
Contents vii
7.7 ParticleSwarmOptimization 316
7.8 Summary 321
Problems 323
8 DiscreteOptimization 327
8.1 Binary,Integer,andDiscreteVariables 327
8.2 AvoidingDiscreteVariables 328
8.3 BranchandBound 330
8.4 GreedyAlgorithms 337
8.5 DynamicProgramming 339
8.6 SimulatedAnnealing 347
8.7 BinaryGeneticAlgorithms 351
8.8 Summary 351
Problems 352
9 MultiobjectiveOptimization 355
9.1 MultipleObjectives 355
9.2 ParetoOptimality 357
9.3 SolutionMethods 358
9.4 Summary 369
Problems 370
10 Surrogate-BasedOptimization 373
10.1 WhentoUseaSurrogateModel 374
10.2 Sampling 375
10.3 ConstructingaSurrogate 384
10.4 Kriging 400
10.5 DeepNeuralNetworks 408
10.6 OptimizationandInfill 414
10.7 Summary 418
Problems 420
11 ConvexOptimization 423
11.1 Introduction 423
11.2 LinearProgramming 425
11.3 QuadraticProgramming 427
11.4 Second-OrderConeProgramming 429
11.5 DisciplinedConvexOptimization 430
11.6 GeometricProgramming 434
11.7 Summary 437
Problems 438
Contents viii
12 OptimizationUnderUncertainty 441
12.1 RobustDesign 442
12.2 ReliableDesign 447
12.3 ForwardPropagation 448
12.4 Summary 469
Problems 471
13 MultidisciplinaryDesignOptimization 475
13.1 TheNeedforMDO 475
13.2 CoupledModels 478
13.3 CoupledDerivativesComputation 501
13.4 MonolithicMDOArchitectures 510
13.5 DistributedMDOArchitectures 519
13.6 Summary 533
Problems 535
A MathematicsBackground 539
A.1 TaylorSeriesExpansion 539
A.2 ChainRule,TotalDerivatives,andDifferentials 541
A.3 MatrixMultiplication 544
A.4 FourFundamentalSubspacesinLinearAlgebra 547
A.5 VectorandMatrixNorms 548
A.6 MatrixTypes 550
A.7 MatrixDerivatives 552
A.8 EigenvaluesandEigenvectors 553
A.9 RandomVariables 554
B LinearSolvers 559
B.1 SystemsofLinearEquations 559
B.2 Conditioning 560
B.3 DirectMethods 560
B.4 IterativeMethods 562
C Quasi-NewtonMethods 571
C.1 Broyden’sMethod 571
C.2 AdditionalQuasi-NewtonApproximations 572
C.3 Sherman–Morrison–WoodburyFormula 576
D TestProblems 579
D.1 UnconstrainedProblems 579
D.2 ConstrainedProblems 586
Contents ix
Bibliography 591
Index 615
Preface
Despiteitsusefulness,designoptimizationremainsunderusedinin-
dustry. Oneofthereasonsforthisistheshortageofdesignoptimization
coursesinundergraduateandgraduatecurricula. Thisischanging;
today,mosttopaerospaceandmechanicalengineeringdepartmentsin-
cludeatleastonegraduate-levelcourseonnumericaloptimization. We
havealsoseendesignoptimizationincreasinglyusedinanexpanding
numberofindustries.
The word engineering in the title reflects the types of problems
andalgorithmswefocuson,eventhoughthemethodsareapplicable
beyond engineering. In contrast to explicit analytic mathematical
functions, most engineering problems are implemented in complex
multidisciplinarycodesthatinvolveimplicitfunctions. Suchproblems
mightrequirehierarchicalsolversandcoupledderivativecomputation.
Furthermore,engineeringproblemsofteninvolvemanydesignvariables
andconstraints,requiringscalablemethods.
Thetargetaudienceforthisbookisadvancedundergraduateand
beginninggraduatestudentsinscienceandengineering. Noprevious
exposure to optimization is assumed. Knowledge of linear algebra,
multivariablecalculus,andnumericalmethodsishelpful. However,
thesesubjects’coreconceptsarereviewedinanappendixandasneeded
inthetext. Thecontentofthebookspansapproximatelytwosemester-
length university courses. Our approach is to start from the most
general case problem and then explain special cases. The first half
ofthebookcoversthefundamentals(alongwithanoptionalhistory
chapter). Incontrast,thesecondhalf,fromChapter8onward,covers
morespecializedoradvancedtopics.
Ourphilosophyintheexpositionistoprovideadetailedenough
explanationandanalysisofoptimizationalgorithmssothatreaders
canimplementabasicworkingversion. Althoughwedonotencourage
∗
InthewordsofDonaldKnuth: “Theul-
readerstousetheirimplementationsinsteadofexistingsoftwarefor
timatetestofwhetherIunderstandsomething
solvingoptimizationproblems,implementingamethodiscrucialin isifIcanexplainittoacomputer. Icansay
∗ somethingtoyouandyou’llnodyourhead,
understandingthemethodanditsbehavior. Adeeperknowledgeof
butI’mnotsurethatIexplaineditwell. But
these methods is useful for developers, researchers, and those who thecomputerdoesn’tnoditshead. Itrepeats
backexactlywhatItellit.Inmostoflife,you
wanttousenumericaloptimizationmoreeffectively. Theproblemsat
canbluff,butnotwithcomputers.”
xi
Preface xii
theendofeachchapteraredesignedtoprovideagradualprogression
indifficultyandeventuallyrequireimplementingthemethods. Some
of the problems are open-ended to encourage students to explore a
giventopicontheirown. Whendiscussingthevariousoptimization
techniques,wealsoexplainhowtoavoidthepotentialpitfallsofusinga
particularmethodandhowtoemployitmoreeffectively. Practicaltips
areincludedthroughoutthebooktoalertthereadertocommonissues
encounteredinengineeringdesignoptimizationandhowtoaddress
them.
We have created a repository with code, data, templates, and
examplesasasupplementaryresourceforthisbook: https://github.
com/mdobook/resources. Someoftheend-of-chapterexercisesrefer
tocodeordatafromthisrepository.
Goforthandoptimize!