def lp10(self, subset_k, subset_p, weights={}):
if self._z is None:<EOL><INDENT>self._add_minimization_vars()<EOL><DEDENT>positive = set(subset_k) - self._flipped<EOL>negative = set(subset_k) & self._flipped<EOL>v = self._v.set(positive)<EOL>cs = self._prob.add_linear_constraints(v >= self._epsilon)<EOL>self._temp_constr.extend(cs)<EOL>v = self._v.set(negative)<EOL>cs = self._prob.add_linear_constraints(v <= -self._epsilon)<EOL>self._temp_constr.extend(cs)<EOL>self._prob.set_objective(self._z.expr(<EOL>(rxnid, -weights.get(rxnid, <NUM_LIT:1>)) for rxnid in subset_p))<EOL>self._solve()<EOL>
Force reactions in K above epsilon while minimizing support of P.

        This program forces reactions in subset K to attain flux > epsilon
        while minimizing the sum of absolute flux values for reactions
        in subset P (L1-regularization).