Problems¶
The Problem class is the entry point to
specifying and solving optimization problems. Each
cvxpy.problems.problem.Problem instance encapsulates an optimization
problem, i.e., an objective and a set of constraints, and the
solve() method either solves the problem
encoded by the instance, returning the optimal value and setting variables
values to optimal points, or reports that the problem was in fact infeasible or
unbounded. You can construct a problem, solve it, and inspect both its value
and the values of its variables like so:
problem = Problem(Minimize(expression), constraints)
problem.solve()
if problem.status not in ["infeasible", "unbounded"]:
# Otherwise, problem.value is inf or -inf, respectively.
print "Optimal value: %s" % problem.value
for variable in problem.variables():
print "Variable %s: value %s" % (variable.name(), variable.value)
Problems are immutable, except through the specification of
Parameter values. This means
that you cannot modify a problem’s objective or constraints after you have
created it. If you find yourself wanting to add a constraint to an existing
problem, you should instead create a new problem using, for example, the
following idiom:
problem = Problem(Minimize(expression), constraints)
problem = Problem(problem.objective, problem.constraints + new_constraints)
Most users need not know anything about the
Problem class except how to instantiate it,
how to solve problem instances (solve()),
and how to query the solver results
(status and
value).
Information about the size of a problem instance and statistics about the most
recent solve invocation are captured by the
SizeMetrics and
SolverStats classes, respectively, and can be
accessed via the size_metrics() and
solver_stats() properties of the
Problem class.
