diff options
Diffstat (limited to '')
| -rw-r--r-- | sumofsquares/problems.py | 91 | ||||
| -rw-r--r-- | sumofsquares/variable.py | 15 |
2 files changed, 78 insertions, 28 deletions
diff --git a/sumofsquares/problems.py b/sumofsquares/problems.py index 8b3eaae..309b13c 100644 --- a/sumofsquares/problems.py +++ b/sumofsquares/problems.py @@ -3,21 +3,25 @@ Specific optimization problems that are related to Sum of Squares. """ from __future__ import annotations -from itertools import filterfalse -from dataclasses import replace +import math +import polymatrix as poly + from dataclassabc import dataclassabc +from dataclasses import replace +from itertools import chain from typing import Any, Sequence from typing_extensions import override -import polymatrix as poly -from polymatrix.expression.expression import Expression, VariableExpression +from polymatrix.expression.expression import Expression, VariableExpression, init_expression from polymatrix.expressionstate.abc import ExpressionState +from polymatrix.statemonad.init import init_state_monad from polymatrix.variable.abc import Variable from .abc import Problem, Constraint, Solver, Result from .constraints import NonNegative from .cvxopt import solve_sos_cone as cvxopt_solve_sos_cone -from .variable import OptVariable +from .utils import partition +from .variable import OptVariable, from_name as opt_variable_from_name @dataclassabc(frozen=True) @@ -62,24 +66,71 @@ class PutinarSOSProblem(Problem): Apply Putinar's positivstellensatz to convert the SOS constraints into coefficient matching constraints """ - def need_positivstellensatz(c): - return isinstance(c, NonNegative) and c.domain + def need_positivstellensatz(c: Constraint) -> bool: + return isinstance(c, NonNegative) and (c.domain is not None) # Add langrange-multiplier-like polynomials to SOS constraints, while # leaving the rest untouched. - variables = list(self.variables) - constraints = filterfalse(need_positivstellensatz, self.constraints) - multipliers = [] - for i, c in enumerate(filter(need_positivstellensatz, self.constraints)): - d = c.expression.degree() - x = poly.v_stack(self.polynomial_variables) - # FIXME: check that there are no variables with this names - # m = x.combinations(d).parametrize(fr"\gamma_{i}") - # multipliers.append(NonNegative(m)) - raise NotImplementedError - - return replace(self, variables=variables, - constraints=tuple(constraints) + tuple(multipliers)) + variables = tuple(self.variables) + constraints, to_process = partition(need_positivstellensatz, self.constraints) + + # New variables and constraints that will be introduced by the + # positivstellensatz + # new_variables = [] + new_constraints = [] + + # Number of variables in the original problem + q = len(self.variables) + x = poly.v_stack((1,) + tuple(self.polynomial_variables)) + + for i, constr in enumerate(to_process): + # For each polynomial that defines the archimedean set + # we create a multiplier polynomial of the same degree + + # FIXME: this is not optimal, look into how to determine the + # smallest degree needed to make the positivstellensatz work. + new_constr = constr.expression + for domain_poly in constr.domain.polynomials: + # To know the degree of the polynomial we need to evaluate the + # expression, hence we use a monad here + def make_multiplier(state): + state, p = domain_poly.degree().apply(state) + + d = p.at(0, 0).constant() + n = sum(math.comb(k + q - 1, k) for k in range(0, d+1)) + + # TODO: check that there is not a variable with this name already + # Coefficients of the multiplier polynomial + c = opt_variable_from_name(rf"\gamma_{i}", shape=(1,n)) + multiplier = c @ x.combinations(tuple(range(d +1))) + + return multiplier.apply(state) + + m = poly.from_statemonad(init_state_monad(make_multiplier)) + + # Subtract multiplier from expression and impose that it is also SOS + new_constr -= m * domain_poly + new_constraints.append(NonNegative(m)) + + new_constraints.append(NonNegative(new_constr)) + + # FIXME: this is a dirty trick to make it work for now, + # it is not efficient, replace with correct solution + + # Convert to polymatrix object to extract variables + new_state = self.state + for constr in new_constraints: + new_state, _ = constr.expression.apply(new_state) + + def is_optvariable(v): + return isinstance(v, OptVariable) + + new_variables = filter(is_optvariable, new_state.indices.keys()) + + return replace(self, + state=new_state, + variables=tuple(new_variables), + constraints=tuple(constraints) + tuple(new_constraints)) @override diff --git a/sumofsquares/variable.py b/sumofsquares/variable.py index 12df4df..6dec3fb 100644 --- a/sumofsquares/variable.py +++ b/sumofsquares/variable.py @@ -60,13 +60,12 @@ def init_opt_variable_expr(name, shape): return OptVariableImpl(name, shape) -def from_names(names: str, shape: tuple[int, int] = (1, 1)) -> tuple[VariableExpression] | VariableExpression: - """ Construct one or multiple variables from comma separated a list of names. """ - variables = tuple(init_variable_expression( - underlying=init_opt_variable_expr(name.strip(), shape)) - for name in names.split(",")) +def from_name(name: str, shape: tuple[int, int] = (1, 1)) -> VariableExpression: + """ Construct an optimization variable. """ + return init_variable_expression(underlying=init_opt_variable_expr(name, shape)) - if len(variables) == 1: - return variables[0] - return variables +def from_names(names: str, shape: tuple[int, int] = (1, 1)) -> Iterable[VariableExpression]: + """ Construct one or multiple variables from comma separated a list of names. """ + for name in names.split(","): + yield from_name(name.strip(), shape) |