Problem-class function

The Problem class.

This class represents a convex optimization problem. class

Problem(objective, constraints = list())

## S4 method for signature 'Problem'
objective(object)

## S4 replacement method for signature 'Problem'
objective(object) <- value

## S4 method for signature 'Problem'
constraints(object)

## S4 replacement method for signature 'Problem'
constraints(object) <- value

## S4 method for signature 'Problem'
value(object)

## S4 replacement method for signature 'Problem'
value(object) <- value

## S4 method for signature 'Problem'
status(object)

## S4 method for signature 'Problem'
is_dcp(object)

## S4 method for signature 'Problem'
is_dgp(object)

## S4 method for signature 'Problem'
is_qp(object)

## S4 method for signature 'Problem'
canonicalize(object)

## S4 method for signature 'Problem'
is_mixed_integer(object)

## S4 method for signature 'Problem'
variables(object)

## S4 method for signature 'Problem'
parameters(object)

## S4 method for signature 'Problem'
constants(object)

## S4 method for signature 'Problem'
atoms(object)

## S4 method for signature 'Problem'
size_metrics(object)

## S4 method for signature 'Problem'
solver_stats(object)

## S4 replacement method for signature 'Problem'
solver_stats(object) <- value

## S4 method for signature 'Problem,character,logical'
get_problem_data(object, solver, gp)

## S4 method for signature 'Problem,character,missing'
get_problem_data(object, solver, gp)

## S4 method for signature 'Problem'
unpack_results(object, solution, chain, inverse_data)

Arguments

• objective: A Minimize or Maximize object representing the optimization objective.
• constraints: (Optional) A list of Constraint objects representing constraints on the optimization variables.
• object: A Problem class.
• value: A Minimize or Maximize object (objective), list of Constraint objects (constraints), or numeric scalar (value).
• solver: A string indicating the solver that the problem data is for. Call installed_solvers() to see all available.
• gp: Is the problem a geometric problem?
• solution: A Solution object.
• chain: The corresponding solving Chain .
• inverse_data: A InverseData object or list containing data necessary for the inversion.

Methods (by generic)

• objective(Problem): The objective of the problem.
• objective(Problem) \<- value: Set the value of the problem objective.
• constraints(Problem): A list of the constraints of the problem.
• constraints(Problem) \<- value: Set the value of the problem constraints.
• value(Problem): The value from the last time the problem was solved (or NA if not solved).
• value(Problem) \<- value: Set the value of the optimal objective.
• status(Problem): The status from the last time the problem was solved.
• is_dcp(Problem): A logical value indicating whether the problem statisfies DCP rules.
• is_dgp(Problem): A logical value indicating whether the problem statisfies DGP rules.
• is_qp(Problem): A logical value indicating whether the problem is a quadratic program.
• canonicalize(Problem): The graph implementation of the problem.
• is_mixed_integer(Problem): logical value indicating whether the problem is a mixed integer program.
• variables(Problem): List of Variable objects in the problem.
• parameters(Problem): List of Parameter objects in the problem.
• constants(Problem): List of Constant objects in the problem.
• atoms(Problem): List of Atom objects in the problem.
• size_metrics(Problem): Information about the size of the problem.
• solver_stats(Problem): Additional information returned by the solver.
• solver_stats(Problem) \<- value: Set the additional information returned by the solver in the problem.
• get_problem_data(object = Problem, solver = character, gp = logical): Get the problem data passed to the specified solver.
• get_problem_data(object = Problem, solver = character, gp = missing): Get the problem data passed to the specified solver.
• unpack_results(Problem): Parses the output from a solver and updates the problem state, including the status, objective value, and values of the primal and dual variables. Assumes the results are from the given solver.

Slots

• objective: A Minimize or Maximize object representing the optimization objective.
• constraints: (Optional) A list of constraints on the optimization variables.
• value: (Internal) Used internally to hold the value of the optimization objective at the solution.
• status: (Internal) Used internally to hold the status of the problem solution.
• .cached_data: (Internal) Used internally to hold cached matrix data.
• .separable_problems: (Internal) Used internally to hold separable problem data.
• .size_metrics: (Internal) Used internally to hold size metrics.
• .solver_stats: (Internal) Used internally to hold solver statistics.

Examples

x <- Variable(2)
p <- Problem(Minimize(p_norm(x, 2)), list(x >= 0))
is_dcp(p)
x <- Variable(2)
A <- matrix(c(1,-1,-1, 1), nrow = 2)
p <- Problem(Minimize(quad_form(x, A)), list(x >= 0))
is_qp(p)