The Problem class. — Problem-class • CVXR

This class represents a convex optimization problem.

Problem(objective, constraints = list())

# S4 method for Problem
objective(object)

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

# S4 method for Problem
constraints(object)

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

# S4 method for Problem
value(object)

# S4 method for Problem
value(object) <- value

# S4 method for Problem
status(object)

# S4 method for Problem
is_dcp(object)

# S4 method for Problem
is_dgp(object)

# S4 method for Problem
is_qp(object)

# S4 method for Problem
canonicalize(object)

# S4 method for Problem
is_mixed_integer(object)

# S4 method for Problem
variables(object)

# S4 method for Problem
parameters(object)

# S4 method for Problem
constants(object)

# S4 method for Problem
atoms(object)

# S4 method for Problem
size_metrics(object)

# S4 method for Problem
solver_stats(object)

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

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

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

# S4 method for 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)
#> [1] TRUE
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)
#> [1] TRUE