snlp.py 文件源码

def __init__(self, model, **kwargs):
        """Initialize a slack form of an :class:`NLPModel`.

        :parameters:
            :model:  Original model to be transformed into a slack form.
        """
        self.model = model

        # Save number of variables and constraints prior to transformation
        self.original_n = model.n
        self.original_m = model.m

        # Number of slacks for the constaints
        n_slacks = model.nlowerC + model.nupperC + model.nrangeC
        self.n_slacks = n_slacks

        # Update effective number of variables and constraints
        n = self.original_n + n_slacks
        m = self.original_m

        Lvar = -np.infty * np.ones(n)
        Uvar = +np.infty * np.ones(n)

        # Copy orignal bounds
        Lvar[:self.original_n] = model.Lvar
        Uvar[:self.original_n] = model.Uvar

        # Add bounds corresponding to lower constraints
        bot = self.original_n
        self.sL = range(bot, bot + model.nlowerC)
        Lvar[bot:bot + model.nlowerC] = model.Lcon[model.lowerC]

        # Add bounds corresponding to upper constraints
        bot += model.nlowerC
        self.sU = range(bot, bot + model.nupperC)
        Uvar[bot:bot + model.nupperC] = model.Ucon[model.upperC]

        # Add bounds corresponding to range constraints
        bot += model.nupperC
        self.sR = range(bot, bot + model.nrangeC)
        Lvar[bot:bot + model.nrangeC] = model.Lcon[model.rangeC]
        Uvar[bot:bot + model.nrangeC] = model.Ucon[model.rangeC]

        # No more inequalities. All constraints are now equal to 0
        Lcon = Ucon = np.zeros(m)

        super(SlackModel, self).__init__(n=n, m=m, name='Slack-' + model.name,
                                         Lvar=Lvar, Uvar=Uvar,
                                         Lcon=Lcon, Ucon=Ucon)

        # Redefine primal and dual initial guesses
        self.original_x0 = model.x0[:]
        self.x0 = np.zeros(self.n)
        self.x0[:self.original_n] = self.original_x0[:]

        self.original_pi0 = model.pi0[:]
        self.pi0 = np.zeros(self.m)
        self.pi0[:self.original_m] = self.original_pi0[:]
        return