All Classes and Interfaces
Class
Description
Provides methods for the Beta distribution, including CDF, PDF, PPF, and SF.
The COptQuestBinaryVariable class defines a binary decision variable which is a
discrete variable that can have a value of 0 or 1.
Provides methods for the Chi-Square distribution: CDF, PDF, and PPF.
The COptQuestCompoundConstraint is a pure virtual class.
COptQuestConstraint is a pure virtual class that defines a linear constraint.
The COptQuestContinuousVariable class defines a decision variable that can have
any value between the lower bound and upper bound.
Design variables are used when value of the decision variable represents an
alternative, and not a quantity.
The CoptQuestDiscreteVariable class defines a decision variable whose values
begin at the lower bound and increment by a step size up to the upper bound
The COptQuestDualRequirement defines a requirement that has both a lower bound
and an upper bound.
The COptQuestEnumerationVariable class defines a decision variable that
has an enumerated set of values such as 5,7 and 25.
The COptQuestEQConstraint class defines an equality linear constraint of the form
2*Var1 + 3*Var2 + 1.5*Var3 = 100
The COptQuestEquationSolver class provides methods to parse strings.
Exceptions thrown by the OptQuest Engine.
COptQuestExchangeableGroup allows the user to create groups of variables that are identical under exchange.
This class provides static methods for computing functions of the exponential distribution,
including the cumulative distribution function (CDF), probability density function (PDF),
percent point function (PPF), survival function (SF), hazard function (HAZ),
and cumulative hazard function (CHAZ).
The COptQuestFrontierMultiObjective allows the user to combine objectives and
treat them as a single objective for the optimization.
Class providing static methods for gamma distribution functions:
CDF, PDF, PPF (inverse CDF), hazard, and cumulative hazard.
The COptQuestGEConstraint class defines a greater than or equal linear constraint
of the form 2*Var1 + 3*Var2 + 1.5*Var3 ≥ 100
The COptQuestGeolocationVariable class defines a decision variable whose values
are lists of latitude and longitude.
The COptQuestIntegerVariable class defines a decision variable whose values
begin at the lower bound and increment by a 1 to the upper bound.
A generic structure to hold the candidate points for a max variance kriging
operation (See GeospataialSamplingGenerator)
This is an implementation of the most common way we'll want to use the
KrigEvaluator.
An implementation of KrigDistance that simply
returns the Euclidean distance between two vectors
A generic structure to hold the candidate points for a
max variance kriging operation (See GeospataialSamplingGenerator)
An implementation of KrigDistance that normalized the variables to be on
[0,1] and computed the Euclidean distance after normalization.
An shared implementation of KrigDistance that handles different types of variables:
right now we handle locations and "default" which is just numeric.
An implementation of KrigDistance that normalized the variables to be on
[0,1] and computed the Euclidean distance after normalization.
An implementation of KrigDistance that returns the "map distance" between two
vectors on the surface of a sphere.
A generic structure to hold the candidate points for a
max variance kriging operation (See GeospataialSamplingGenerator)
This is the an implementaion of a KrigTestPointSequence
Upon construction it is given an array of points that it will
return in order every time ::next() is called.
The COptQuestLEConstraint class defines a less than or equal linear constraint of
the form 2*Var1 + 3*Var2 + 1.5*Var3 ≤ 100
The COptQuestLowerRequirement defines a requirement that has a lower bound but
no upper bound.
The COptQuestMultiObjective is an abstract class.
The COptQuestObjective class is a pure virtual class that defines the objective
of the optimization.
The COptQuestObjectiveFunction class is used to define a linear objective.
The COptQuestOptimization class searches for solutions to problems using a
'black box' approach.
The COptQuestOrConstraint class allows you to define a logical "or" relationship
among COptQuestConstraint objects.
The COptQuestOrRequirement class allows you to define a logical "or" relationship
among COptQuestRequirement objects.
Pareto distribution implementation.
This class allows users to add groups of permutation variables to an
optimization problem.
Permutation variables are used to solve sequencing problems.
The COptQuestRequirement class is a pure virtual class that allows you to define
a non-linear constraint.
The COptQuestSearchParameters class contains values that control
the search algorithms.
COptQuestSelectionGroup allows the user to have a binary variable who value indicates an on/off state,
where the off state indicates that other variables are not used in the evaluation.
A COptQuestSolution object contains the values for one solution and provides
methods to access information on the solution.
The COptQuestSolutionSet class allows you to select a set of solutions
and perform statistical analysis or sensitivity analysis on the
set.
The COptQuestStringConstraint class is used to define linear and non-linear
constraints using a mathematical expression.
The COptQuestStringObjective class is used to define an objective function
using a mathematical expression.
COptQuestStringVariable allows the user to add a variable that defines an expression where the
expression is a mathematical formula that can reference input variables, or output variables by their names.
The COptQuestTupleVariable class defines a decision variable whose values
are lists of tuples.
The COptQuestUpperRequirement defines a requirement that has an upper bound but
no lower bound.
The COptQuestUserControlledObjective allows you to compute a value for
the optimization objective.
The COptQuestUserControlledOptimization class searches for solutions to problems using a
'black box' approach.
The COptQuestUserControlledVariable class allows you to define a variable where
you set the value of the variable.
COptQuestVariable is a pure virtual class that defines a decision variable.
Provides static methods for computing various functions of the Weibull distribution.
Deprecated.
A generic interface to return the generalized distance between two vectors
For Kriging, we need to know the distance between two vectors.
A generic interface to return the generalized distance between two vectors
For Kriging, we need to know the covariance between two vectors, usually
based on distance (see KrigDistance).
ISolutionFilter is used to define a filter for a COptQuestSolutionSet object.