Solver-class: Solver class
In prioritizr: Systematic Conservation Prioritization in R
Solver-class R Documentation
Solver class
Description
This class is used to represent solvers for optimization.
Only experts should use the fields and methods for this class directly.
Super class
ConservationModifier -> Solver
Methods
Public methods
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Inherited methods
Solver$run()
Run the solver to generate a solution.
Usage
Solver$run()
Returns
list of solutions.
Solver$calculate()
Perform computations that need to be completed before applying
the object.
Usage
Solver$calculate(...)
Arguments
...Additional arguments.
xoptimization_problem() object.
Returns
Invisible TRUE.
Solver$set_variable_ub()
Set the upper bound for a decision variable.
Usage
Solver$set_variable_ub(index, value)
Arguments
indexinteger value indicating the index of the decision
variable.
valuenumeric new bound value.
Details
Note that this method should only be run after $calculate().
It can be used to overwrite values after ingesting an
optimization_problem() object.
It is designed to be used in portfolios and importance functions.
Returns
Invisible TRUE.
Solver$set_variable_lb()
Set the lower bound for a decision variable.
Usage
Solver$set_variable_lb(index, value)
Arguments
indexinteger value indicating the index of the decision
variable.
valuenumeric new bound value.
Details
Note that this method should only be run after $calculate().
It can be used to overwrite values after ingesting an
optimization_problem() object.
It is designed to be used in portfolios and importance functions.
Returns
Invisible TRUE.
Solver$set_constraint_rhs()
Set the right-hand-side coefficient bound for a constraint.
Usage
Solver$set_constraint_rhs(index, value)
Arguments
indexinteger value indicating the index of the decision
variable.
valuenumeric new value.
Details
Note that this method should only be run after $calculate().
It can be used to overwrite values after ingesting an
optimization_problem() object.
It is designed to be used in portfolios and importance functions.
Returns
Invisible TRUE.
Solver$set_start_solution()
Set the starting solution.
Usage
Solver$set_start_solution(value, warn = TRUE)
Arguments
valuenumeric vector.
warnlogical indicating if a warning should be displayed
if the solver does not support starting solutions.
Details
This method is designed used in portfolios and importance
functions.
Returns
Invisible TRUE.
Solver$remove_start_solution()
Remove the starting solution.
Usage
Solver$remove_start_solution()
Details
This method is designed used in portfolios and importance
functions.
Returns
Invisible TRUE.
Solver$solve()
Solve an optimization problem.
Usage
Solver$solve(x, ...)
Arguments
xoptimization_problem() object.
...Additional arguments passed to the calculate() method.
Returns
A list object with the solution and additional information.
Solver$default_solve_multiobj()
Solve a multi-objective optimization problem using a hierarchical
multi-objective optimization approach.
Broadly speaking, this approach involves using multiple optimization
procedures to solve objectives following a hierarchical (lexicographic)
ordering, wherein those associated with a higher priority order are
solved before those with a lower priority order. When implementing this
approach, constraints are added after generating a given solution to
ensure that subsequent solutions for lower priority objectives
have adequate performance according to higher priority objectives.
Usage
Solver$default_solve_multiobj(x, priority, rel_tol, ...)
Arguments
xlist object with multi-objective optimization problem.
Arguments must contain the following elements:
("opt") OptimizationProblem object;
("modelsense") character vector containing the model sense values
for each objective; and ("obj") numeric' matrix containing the
coefficients for each of the objectives, wherein rows correspond to
different objectives, columns to different decision variables and
row names can be optionally specify names for the objectives.
prioritynumeric vector with values indicating the
priority for each objective. Greater values denote greater priority,
and so objectives associated with greater values are optimized
earlier in the multi-objective process.
rel_tolnumeric vector with relative tolerance values
for each constraint. Greater values denote a greater degree of
sub-optimality.
...Additional arguments passed to the calculate() method.
Returns
A list object with the solution and additional information.
Solver$solve_multiobj()
Solve a multi-objective optimization problem using a hierarchical
multi-objective optimization approach.
Broadly speaking, this approach involves using multiple optimization
procedures to solve objectives following a hierarchical (lexicographic)
ordering, wherein those associated with a higher priority order are
solved before those with a lower priority order. When implementing this
approach, constraints are added after generating a given solution to
ensure that subsequent solutions for lower priority objectives
have adequate performance according to higher priority objectives.
Usage
Solver$solve_multiobj(x, priority, rel_tol, ...)
Arguments
xlist object with multi-objective optimization problem.
Arguments must contain the following elements:
("opt") OptimizationProblem object;
("modelsense") character vector containing the model sense values
for each objective; and ("obj") numeric' matrix containing the
coefficients for each of the objectives, wherein rows correspond to
different objectives, columns to different decision variables and
row names can be optionally specify names for the objectives.
prioritynumeric vector with values indicating the
priority for each objective. Greater values denote greater priority,
and so objectives associated with greater values are optimized
earlier in the multi-objective process.
rel_tolnumeric vector with relative tolerance values
for each constraint. Greater values denote a greater degree of
sub-optimality.
...Additional arguments passed to the calculate() method.
Returns
A list object with the solution and additional information.
Solver$clone()
The objects of this class are cloneable with this method.
Usage
Solver$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Other classes:
ConservationModifier-class,
ConservationProblem-class,
Constraint-class,
Decision-class,
MultiConservationProblem-class,
MultiObjApproach-class,
Objective-class,
OptimizationProblem-class,
Penalty-class,
Portfolio-class,
Target-class,
TargetMethod-class,
Weight-class
prioritizr documentation built on Sept. 24, 2026, 5:07 p.m.
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| Solver-class | R Documentation |
Solver class
Description
This class is used to represent solvers for optimization. Only experts should use the fields and methods for this class directly.
Super class
ConservationModifier -> Solver
Methods
Public methods
Inherited methods
Solver$run()
Run the solver to generate a solution.
Usage
Solver$run()
Returns
list of solutions.
Solver$calculate()
Perform computations that need to be completed before applying the object.
Usage
Solver$calculate(...)
Arguments
...Additional arguments.
xoptimization_problem()object.
Returns
Invisible TRUE.
Solver$set_variable_ub()
Set the upper bound for a decision variable.
Usage
Solver$set_variable_ub(index, value)
Arguments
indexintegervalue indicating the index of the decision variable.valuenumericnew bound value.
Details
Note that this method should only be run after $calculate().
It can be used to overwrite values after ingesting an
optimization_problem() object.
It is designed to be used in portfolios and importance functions.
Returns
Invisible TRUE.
Solver$set_variable_lb()
Set the lower bound for a decision variable.
Usage
Solver$set_variable_lb(index, value)
Arguments
indexintegervalue indicating the index of the decision variable.valuenumericnew bound value.
Details
Note that this method should only be run after $calculate().
It can be used to overwrite values after ingesting an
optimization_problem() object.
It is designed to be used in portfolios and importance functions.
Returns
Invisible TRUE.
Solver$set_constraint_rhs()
Set the right-hand-side coefficient bound for a constraint.
Usage
Solver$set_constraint_rhs(index, value)
Arguments
indexintegervalue indicating the index of the decision variable.valuenumericnew value.
Details
Note that this method should only be run after $calculate().
It can be used to overwrite values after ingesting an
optimization_problem() object.
It is designed to be used in portfolios and importance functions.
Returns
Invisible TRUE.
Solver$set_start_solution()
Set the starting solution.
Usage
Solver$set_start_solution(value, warn = TRUE)
Arguments
valuenumericvector.warnlogicalindicating if a warning should be displayed if the solver does not support starting solutions.
Details
This method is designed used in portfolios and importance functions.
Returns
Invisible TRUE.
Solver$remove_start_solution()
Remove the starting solution.
Usage
Solver$remove_start_solution()
Details
This method is designed used in portfolios and importance functions.
Returns
Invisible TRUE.
Solver$solve()
Solve an optimization problem.
Usage
Solver$solve(x, ...)
Arguments
xoptimization_problem()object....Additional arguments passed to the
calculate()method.
Returns
A list object with the solution and additional information.
Solver$default_solve_multiobj()
Solve a multi-objective optimization problem using a hierarchical multi-objective optimization approach. Broadly speaking, this approach involves using multiple optimization procedures to solve objectives following a hierarchical (lexicographic) ordering, wherein those associated with a higher priority order are solved before those with a lower priority order. When implementing this approach, constraints are added after generating a given solution to ensure that subsequent solutions for lower priority objectives have adequate performance according to higher priority objectives.
Usage
Solver$default_solve_multiobj(x, priority, rel_tol, ...)
Arguments
xlistobject with multi-objective optimization problem. Arguments must contain the following elements: ("opt")OptimizationProblemobject; ("modelsense")charactervector containing the model sense values for each objective; and ("obj") numeric' matrix containing the coefficients for each of the objectives, wherein rows correspond to different objectives, columns to different decision variables and row names can be optionally specify names for the objectives.prioritynumericvector with values indicating the priority for each objective. Greater values denote greater priority, and so objectives associated with greater values are optimized earlier in the multi-objective process.rel_tolnumericvector with relative tolerance values for each constraint. Greater values denote a greater degree of sub-optimality....Additional arguments passed to the
calculate()method.
Returns
A list object with the solution and additional information.
Solver$solve_multiobj()
Solve a multi-objective optimization problem using a hierarchical multi-objective optimization approach. Broadly speaking, this approach involves using multiple optimization procedures to solve objectives following a hierarchical (lexicographic) ordering, wherein those associated with a higher priority order are solved before those with a lower priority order. When implementing this approach, constraints are added after generating a given solution to ensure that subsequent solutions for lower priority objectives have adequate performance according to higher priority objectives.
Usage
Solver$solve_multiobj(x, priority, rel_tol, ...)
Arguments
xlistobject with multi-objective optimization problem. Arguments must contain the following elements: ("opt")OptimizationProblemobject; ("modelsense")charactervector containing the model sense values for each objective; and ("obj") numeric' matrix containing the coefficients for each of the objectives, wherein rows correspond to different objectives, columns to different decision variables and row names can be optionally specify names for the objectives.prioritynumericvector with values indicating the priority for each objective. Greater values denote greater priority, and so objectives associated with greater values are optimized earlier in the multi-objective process.rel_tolnumericvector with relative tolerance values for each constraint. Greater values denote a greater degree of sub-optimality....Additional arguments passed to the
calculate()method.
Returns
A list object with the solution and additional information.
Solver$clone()
The objects of this class are cloneable with this method.
Usage
Solver$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Other classes:
ConservationModifier-class,
ConservationProblem-class,
Constraint-class,
Decision-class,
MultiConservationProblem-class,
MultiObjApproach-class,
Objective-class,
OptimizationProblem-class,
Penalty-class,
Portfolio-class,
Target-class,
TargetMethod-class,
Weight-class
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