- a
- Aberrations
- Cliffs (vertical walls)
- Hard to Find Optimum
- Infeasible Calculations
- Level Spots
- Multiple Optima
- Noise–Stochastic Response
- of Response Surface
- Sharp Valleys (or ridges)
- Striations
- Uniform Minimum
- Accelerate
- Accumulation Along a Path
- Acknowledgments
- Acronyms
- Algebra and Assignment Statements
- Analytical Method (AM)
- Annualized Capital
- a posteriori
- Appendices
- Application Classifications
- a priori
- Attenuate
- Autocorrelated
- b
- Backward Difference
- Belongingness
- Best
- Best‐of‐N
- BFGS see Broyden–Fletcher–Goldfarb–Shanno (BFGS)
- Bisection Method
- Bloom’s Taxonomy of Cognitive Skill
- Book, Aspirations
- Book, aspirations
- Bootstrapping
- Appropriating
- Basis
- Model uncertainty
- Result interpretation
- Bottom Line
- Broyden–Fletcher–Goldfarb‐Shanno (BFGS)
- c
- Canonical optimization statement
- Case Study
- Area Coverage
- Data Reconciliation
- Goddard Rocket Study
- In‐Vitro Fertilization
- Liquid‐Vapor Separator
- ODE Solution Approximation
- Pipe System Optimization
- Queuing
- Reservoir
- Retirement
- Cauchy’s Sequential Line Search (CSLS)
- Commentary
- with Golden Section
- with Heuristic Direct Search
- with Leapfrogging
- with Newton/Secant
- Pseudocode
- with Successive Quadratic
- VBA Code
- CDF see Cumulative distribution function (CDF)
- Central Difference
- Central Limit Theorem
- CG see Conjugate Gradient (CG)
- Challenge Problems see also Test Functions
- Challenges to Optimizers
- CHD see Cyclic Heuristic Direct Search (CHD)
- Choices
- Chromosome
- Classification
- Application
- Category OFs
- Convergence
- Models
- Optimization Issues
- Optimizer
- Variables
- Class Variables
- Cliffs Ridges/Valleys
- CM see Complex Method (CM)
- Coarse‐Fine Sequence
- Companion Web Site
- Compare
- CHD & HJ
- CSLS, ISD, NR, LM, RLM, CHD, HJ, LF, PSO
- CSLS, ISD, SQ, and NR
- LF, HJ, LM, PSO
- NR & ISD
- Optimizers
- Comparison
- Convergence Criteria
- Direct Search w Single Trial Solution
- Gradient Based
- Local Surface Characterization Based
- Multi‐Player Direct Search Optimizers
- Optimizers and Attributes
- Complexity Factor
- Complex Method (CM)
- Conditionals, Discontinuities
- Conjugate Gradient (CG)
- Constituents (Customer Stakeholders)
- Constraints
- Active
- Analysis
- Equality
- Equivalent Features
- Explicit
- Hard
- Implicit
- Inactive
- Inequality
- Pass/Fail Categories
- Penalty Function
- Penalty Function Soft
- Slack and Surplus Variables
- Soft
- Continuum and Discontinuous DVs
- Continuum and Discontinuous Models
- Contour lines
- Convergence
- on OF
- on Change in DV
- on Change in OF
- Choosing Threshold Values
- Classifications
- on Combinations of Criteria
- on Comparing Change in OF to Experimental Uncertainty
- on Comparing Change in OF to Uncertainty in Givens
- Criteria, Comparison
- Criteria 1‐D Applications
- Multiplayer Deterministic
- on Multi‐Player Range
- N‐D Criteria
- Other Criteria
- premature
- on Random Subset Sampling
- on Relative Change in DV
- on Relative Change in OF
- on Sensitivity of DV to OF
- Single TS Deterministic
- on Steady State
- Stochastic Applications
- Convex
- Criteria
- Convergence 1‐D Applications
- for Replicate Trials
- Stopping 1‐D Applications
- CSLS see Cauchy’s Sequential Line Search (CSLS)
- Cumulative Distribution Function (CDF)
- Customer (Stakeholder, Constituents)
- Cyclic Heuristic Direct Search (CHD)
- d
- Decision Variable (DV)
- Defining an Iteration
- Definitions
- Degree of Freedom
- Derivative
- Designing Experiments, test Best‐of‐N
- Desirables
- Desired Engineering Attributes
- Developing/Evolving Application Statements
- Direct Search
- Bisection Method
- Golden Section Method
- Heuristic Direct Search
- Leapfrogging
- LF for Stochastic Functions
- Multivariable, Multi‐Player
- Multivariable, Single TS
- Discontinuities, Conditionals
- Discontinuous and Continuum
- Discount Factor
- Discrete and Integer Variables
- Branch‐and‐Bound
- Convergence
- Cyclic Heuristic
- Exhaustive Search
- Leapfrogging
- Multi‐Player
- Discretization
- Dominated
- Do not Study
- DP see Dynamic Programming (DP)
- DV see Decision Variable (DV)
- Dynamic and Static Models
- Dynamic (transient) Model
- Dynamic Programming (DP)
- Concept
- Conditions
- Some Calculation Tips
- e
- Economic Optimization
- Annual Cash Flow
- Capital
- Combining Capital and Cash Flow
- Present Value
- Risk
- Time Value
- Uncertainty
- Effects of Uncertainty
- Elements Associated with Optimization
- Enhancements to Optimizers
- Equal Concern (EC) Weighting
- Evaluating
- Evaluating 2nd Order Partial Derivatives
- Evaluating Optimizers
- Designing an Experimental Test
- Metrics of Performance
- Evaluating Results
- Evaluating 1st Order Derivatives
- Experimental OF
- Exponentially Weighted Moving Average (EWMA)
- f
- Feasible
- Feasible Solutions
- Final Prediction Error (FPE)
- First‐Order Filter
- Fitness
- Flat spots
- Forward Differences
- FPE see Final Prediction Error (FPE)
- Fuzzy Logic
- g
- GA see Genetic/Evolutionary Algorithms (GA)
- Gamma Function
- Gauss Elimination
- Pivoting
- Procedure
- VBA Code
- Gaussian Distributed (NID Box‐Muller)
- Gene
- Generalized Reduced Gradient (GRG)
- Genetic/Evolutionary Algorithms (GA)
- Fitness of Selection
- Procedures
- Givens
- Global Attractor
- Global Optimizer
- Global Optimum
- Golden Ratio, Side Note
- Golden Section Method (GS)
- Gradient
- Gradient‐Based Optimizers (CSLS and ISD)
- Gradient Based Optimizer Solutions
- Greedy Algorithm
- GRG see Generalized Reduced Gradient (GRG)
- GS see Golden Section Method (GS)
- h
- Hard constraints
- Heuristic Direct (HD)
- Hooke‐Jeeves (HJ)
- Human Contrivances
- i
- Implicit/explicit Relations
- Incremental Steepest Descent (ISD)
- Enhanced
- Illustration
- Pseudo‐Code
- VBA Code
- Inequality Constraints
- Inflections
- Initialization Range
- In‐Process Adjustment of Algorithm Coefficient
- Integer DV
- Integers
- Intellectual Exercise
- Interval Halving
- Introductory Concepts
- Intuitive Decisions
- Intuitive Optimization
- Optimization Levels
- ISD see Incremental Steepest Descent (ISD)
- Iteration
- Iterative Procedures
- l
- Lagrange‐Type Multiplier
- Landmark Publications
- Leapfrogging (LF)
- Analysis
- Balance
- Convergence Criteria
- Leap‐To Window Amplification
- Leap‐to Window Translation
- Minimize NOFE
- Number of Initializations
- Premature Convergence
- Probability Distribution of Leap‐overs
- Stochastic Functions
- Levenberg‐Marquardt (LM)
- Modified (RLM)
- VBA Code for 2‐DV case
- LF see Leapfrogging (LF)
- Linear Algebra Notation
- Linear Equation Sets
- Linearity and Nonlinearity
- Linearizing Data Transform
- Linear Programming (LP)
- Algorithm
- Basic Procedure
- Canonical Statement
- Simplex Tableau
- Line Search (Univariate Search)
- Linguistic Rules (Fuzzy Logic)
- LM see Levenberg‐Marquardt (LM)
- Local Optimum
- Logistic Model
- Long Term Return on Assets
- LP see Linear Programming (LP)
- m
- Mathematical Representation of Relations
- Membership function
- Memorization, Overfitting
- Mimetic
- Min‐Max, Max‐Min
- Model
- Modified Levenberg‐Marquardt (RLM)
- Pseudo‐Code
- VBA Code for 2‐DV case
- Modified OF
- Impact on NOFE
- Max‐Min Equivalence
- Scaling
- OF Transformation
- Translating
- Monte Carlo
- Multi‐Dimension
- Multi‐Optima
- Multi‐Player
- Multiple Objectives
- Additive Combination
- Classic Weighting Factors
- Constraint Applications
- Equal Concern Weighting
- Non‐Additive OF Combinations
- Nonlinear Weighting
- Pareto Optimal
- Multiple Starts
- Best‐of‐N
- Other Options
- a posteriori Method
- a priori Method
- Snyman and Fatti Criterion
- Multivariate Search
- Mutation
- n
- Nature Compliance to Human Contrivances
- N‐D Notation
- Nelder‐Mead (NM)
- Nested Optimization
- Net lines
- Net Present Value (NPV)
- Newton Interpolating Polynomial
- Newton‐Methods
- Newton Raphson (NR)
- Attenuate
- Pseudo Code
- Quasi‐Newton
- Tempering
- Newton‐Secant
- NM see Nelder‐Mead (NM)
- NOFE see Number of Function Evaluations (NOFE)
- Noisy Steady State
- Nomenclature
- Non‐Additive Cost Functions
- Non‐Convex
- Non‐Dominated
- Nonlinear Regression
- Nonparametric, Statistics
- Normal Equations (Linear Regression)
- NPV see Net Present Value (NPV)
- NR see Newton Raphson (NR)
- Number of Function Evaluations (NOFE)
- Number of Independent Trials
- Number of Players
- Numerical Derivative, Step Size
- Numerical Iterative Procedures
- o
- Objective Function (OF)
- OF see Objective Function (OF)
- Opposing
- functionalities
- Ideals
- trends
- Optimization
- Definitions
- Discrete and Integer Variables
- Introduction
- Issues
- Key Points
- Probable Outcomes and Distribution Characteristics
- Procedure
- Stages
- Terminology
- Optimizer
- Over‐Specification
- Linear Relations
- Optimization
- p
- Parameter Correlation
- Parametric Path Notation
- Parametric, statistics
- Pareto Optimal
- Partial Derivatives 1st Order
- Particle Swarm Optimization (PSO)
- Equation Analysis
- Individual Particle Behavior
- Position Mode
- Velocity mode
- Path
- Accumulation
- Analysis
- Integral
- Parametric Notation
- Slope
- Pay‐Back Time
- Penalty Functions
- Perfection/Sufficiency
- Persistence Time‐Constant
- Perspective on Many topics
- Phantom (fortuitous) OF value
- PNOFE see Probable Number of Function Evaluations (PNOFE)
- Point Values
- Positional Invariance, “Theory” of
- Precision
- Preface
- Present Value
- Pretend
- Primer for VBA Programming
- Probable Number of Function Evaluations (PNOFE)
- Probable Outcomes and Distribution Characteristics
- Probable Outcomes, Stochastic Approach
- Procedures, not Equations
- Proficiency
- Proximity (Vicinity)
- PSO see Particle Swarm Optimization (PSO)
- Publications, Landmark
- r
- Random Keys Method
- Dichotomous Variables
- Sequence
- Rank
- Realization
- Recursion Formula
- Reduced Gradient
- References
- Regression
- Akaho’s Method
- Convergence Criterion
- Least Squares
- Maximum Likelihood
- Model Order or Complexity
- Models that are Nonlinear in DVs
- Models with Delay as a DV
- Normal Least Squares
- Total Least Squares
- Vertical Least Squares
- Reliability
- Replicates
- Replicate Trials, Criteria
- Response Surface Aberrations see also Aberrations
- Risk
- RLM see Modified Levenberg–Marquardt (RLM)
- Rms see root mean square (rms)
- Root‐Finding
- on derivative
- w.r.t. Optimization
- root mean square (rms)
- s
- Saddle Point
- Scaled Variables
- Dimensional Consistency
- Issues with Primitive Variables
- Linear Scaling Options
- Nonlinear Scaling
- Search Range Adjustment
- Second‐Order Methods 1‐D
- Second‐Order model Based Optimizers: SQ & NR
- SEE see sketch evaluate erase (SEE)
- SHH see Spendley‐Hext‐Himsworth (SHH)
- Significant Dates
- Single TS
- sketch evaluate erase (SEE)
- Slack Variable
- Slope Along a Path
- Soft Constraints
- Solution of a System of Linear Equations
- Solution Precision
- Spendley‐Hext‐Himsworth (SHH)
- SQ see Successive Quadratic (SQ)
- SSID see Steady State Identification in Noisy Signals (SSID)
- Stage Variable
- Stakeholder (Constituent Customer)
- State Variable
- Static and Dynamic Models
- Stationary Point
- Steady State Identification in Noisy Signals (SSID)
- Alternate Type‐I Error
- Array Method
- Coefficient Threshold and Sample Frequency Values
- Conceptual Model
- Data autocorrelation
- Filter Method Code
- Filter Method Equations
- R‐Statistic
- Type‐I Error
- Type‐II Error
- VBA Code Array Method
- VBA Code Filter Method
- Steady State Model
- Steepest Descent
- Stochastic Functions
- Method For Optimizing
- Replicate the Apparent Best Player
- Reporting
- Steady State Detection
- Stochastic Surfaces
- Stopping Criteria
- Constraint Violation
- End a Futile Search
- Execution Error
- N Iterations
- Successive Quadratic (SQ)
- Multivariable
- Pseudo Code
- Sufficiency/Perfection
- Sum of Absolute Value of Deviations
- Sum of Squared Residuals (Deviations)
- Surface Aberrations see also Aberrations
- Surface Analysis
- Surface and Terms
- Surplus Variable
- Surrogate Function
- Surrogate Population
- Survival of the fittest
- Sustainable
- Symbols
- t
- Tabu
- Tangent to Contour
- Taylor Series
- Expansion for a Single Variable
- Expansion for Multiple Variables
- Tempering Newton‐Type methods
- Test Functions
- Algae Pond
- ARMA(2,1) Regression
- Boot Print
- Boot Print w Pinhole
- Chemical Reaction
- Chong Vu’s Normal Regression
- Classroom Lecture Pattern
- Cork Board
- Curved Sharp Valley
- Frog
- Goddard Problem
- H & C Mixing Control
- Hose Winder
- Integer Problem
- Liquid‐Vapor Separator
- Mountain Path
- Other Sources
- Parallel Pumps
- Parameter Correlation
- Peaks
- Rank Model
- Reliability
- Reservoir
- Retirement
- Robot Soccer
- Shortest Time
- Slingshot into Space
- Solving an ODE
- Stochastic Boot Print
- Three Springs
- Underspecified
- Windblown
- Textbooks
- Optimization
- Probability and Statistics
- Simulation
- Specific Techniques
- Time Value of Money
- Transformations OF and DV
- Transformed DVs
- Traveling Salesman Problem (TSP)
- Trial‐and‐Error
- Trial Solution (TS)
- Troubleshooting
- CDF Features
- Concern Over Results
- DV* is dependent on Convergence Threshold
- DV values do not change
- EXE Error
- Extreme Values
- Multiple DV* values for the same OF* Value
- Multiple Equivalent Solutions
- OF* is Irreproducible
- Parameter Correlation
- TSP
- Two‐D Parametric Notation
- u
- UID see Uniform Distributed (UID)
- Uncertainty
- Analytical Method
- Bootstrapping
- in DV* and OF*
- Estimating Values
- Implicit/explicit Relations
- Modeling Concept
- Models for
- Numerical Method
- in Optimization
- Propagating Max Error
- Propagating on DVs
- Propagating Probable Error
- Propagation of Variance
- Significant Digits
- Sources
- Uncertainty Effects
- Under‐Specification
- Linear Relations
- Optimization
- Undesirables
- Uniform Distributed (UID)
- Univariate Methods
- v
- Validation
- Variable Classification
- Cardinal
- Discretization
- Nominal
- Ordinal
- Scaled
- VBA
- Calling Solver from VBA
- Conditionals
- Debugging
- External File I/O
- I/O to Excel Cells
- Keystroke Macros
- Loops
- Objects and Properties
- Operations
- Primer
- Run buttons (Commands)
- Solver Add‐In
- Variable Types and Declarations
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