Class: LpSolver::Solution

Inherits:
Object
  • Object
show all
Defined in:
lib/lpsolver/solution.rb

Overview

Represents the solution returned by solving a model.

The Solution object contains the optimal (or best-found) values for all decision variables, the optimal objective value, and metadata about the solver’s execution.

Examples:

Accessing solution values

solution = model.solve
solution[:x]        # => 4.0 (value of variable x)
solution[:y]        # => 0.0 (value of variable y)
solution.objective_value  # => 12.0 (optimal objective)

Checking solution status

solution.feasible?      # => true
solution.infeasible?    # => false
solution.unbounded?     # => false

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(variables:, objective_value:, model_status:, iterations:) ⇒ Solution

Creates a new Solution object.

Parameters:

  • variables (Hash{String => Float}) —

    Maps variable names to values.

  • objective_value (Float) —

    The optimal objective value.

  • model_status (String) —

    The solver-reported model status.

  • iterations (Integer) —

    The number of solver iterations.



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# File 'lib/lpsolver/solution.rb', line 49

def initialize(variables:, objective_value:, model_status:, iterations:)
  @variables = variables
  @objective_value = objective_value
  @model_status = model_status
  @iterations = iterations
end

Instance Attribute Details

#iterations ⇒ Integer (readonly)

Returns The number of iterations the solver performed. This is a diagnostic metric; may be 0 for some solver types.

Returns:

  • (Integer) —

    The number of iterations the solver performed. This is a diagnostic metric; may be 0 for some solver types.



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# File 'lib/lpsolver/solution.rb', line 41

def iterations
  @iterations
end

#model_status ⇒ String (readonly)

Returns The status of the model as reported by HiGHS. Possible values include:

  • “optimal”: An optimal solution was found.

  • “infeasible”: No feasible solution exists.

  • “unbounded”: The objective can be improved without bound.

  • “unknown”: The solver could not determine the status.

Returns:

  • (String) —

    The status of the model as reported by HiGHS. Possible values include:

    • “optimal”: An optimal solution was found.

    • “infeasible”: No feasible solution exists.

    • “unbounded”: The objective can be improved without bound.

    • “unknown”: The solver could not determine the status.



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# File 'lib/lpsolver/solution.rb', line 37

def model_status
  @model_status
end

#objective_value ⇒ Float (readonly)

Returns The optimal objective function value. For minimization problems, this is the minimum value. For maximization problems, this is the maximum value.

Returns:

  • (Float) —

    The optimal objective function value. For minimization problems, this is the minimum value. For maximization problems, this is the maximum value.



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# File 'lib/lpsolver/solution.rb', line 29

def objective_value
  @objective_value
end

#variables ⇒ Hash{String => Float} (readonly)

Returns Maps variable names to their optimal values. The keys are the variable names as strings (as produced by HiGHS), and the values are the optimal decision variable values.

Returns:

  • (Hash{String => Float}) —

    Maps variable names to their optimal values. The keys are the variable names as strings (as produced by HiGHS), and the values are the optimal decision variable values.



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# File 'lib/lpsolver/solution.rb', line 24

def variables
  @variables
end

Instance Method Details

#[](name) ⇒ Float

Retrieves the value of a variable by name.

Examples:

solution[:x]  # => 4.0

Parameters:

  • name (Symbol, String) —

    The variable name.

Returns:

  • (Float) —

    The optimal value of the variable.

Raises:

  • (KeyError) —

    If the variable name is not found in the solution.



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# File 'lib/lpsolver/solution.rb', line 63

def [](name)
  variables[name.to_s]
end

#feasible? ⇒ Boolean

Checks if the solution is feasible.

A solution is feasible if the solver found a solution that satisfies all constraints and bounds.

Returns:

  • (Boolean) —

    True if the model status is “optimal”.



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# File 'lib/lpsolver/solution.rb', line 84

def feasible?
  @model_status == 'optimal'
end

#infeasible? ⇒ Boolean

Checks if the model is infeasible.

A model is infeasible if no solution exists that satisfies all constraints simultaneously.

Returns:

  • (Boolean) —

    True if the model status is “infeasible”.



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# File 'lib/lpsolver/solution.rb', line 94

def infeasible?
  @model_status == 'infeasible'
end

#to_s ⇒ String

Returns a string representation of the solution.

Examples:

puts solution
# x = 4.0
# y = 0.0
# Objective: 12.0

Returns:

  • (String) —

    A formatted string showing variable values and the objective value.



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# File 'lib/lpsolver/solution.rb', line 117

def to_s
  lines = variables.map { |name, value| "#{name} = #{value}" }
  lines << "Objective: #{objective_value}"
  lines.join("\n")
end

#unbounded? ⇒ Boolean

Checks if the model is unbounded.

A model is unbounded if the objective can be improved indefinitely without violating any constraints.

Returns:

  • (Boolean) —

    True if the model status is “unbounded”.



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# File 'lib/lpsolver/solution.rb', line 104

def unbounded?
  @model_status == 'unbounded'
end

#values_at(*names) ⇒ Array<Float>

Retrieves values for multiple variables by name.

Examples:

solution.values_at(:x, :y)  # => [4.0, 0.0]

Parameters:

  • *names (Symbol, String) —

    The variable names to retrieve.

  • names (Array<Object>)

Returns:

  • (Array<Float>) —

    An array of variable values in the same order.



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# File 'lib/lpsolver/solution.rb', line 74

def values_at(*names)
  names.map { |name| variables[name.to_s] }
end