Python cvxpy.Constant方法代码示例

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Constant [as 别名]
def loss(self, A, U): return cp.norm(cp.Constant(A) - U, "fro")/2.0 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Constant [as 别名]
def __str__(self): return "huber loss"

# class FractionalLoss(Loss):
#     PRECISION = 1e-10
#     def loss(self, A, U):
#         B = cp.Constant(A)
#         U = cp.max_elemwise(U, self.PRECISION) # to avoid dividing by zero
#         return cp.max_elemwise(cp.mul_elemwise(cp.inv_pos(cp.pos(U)), B-U), \
#         return maximum((A - U)/U, (U - A)/A)
# 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Constant [as 别名]
def _initialize_probs(self, A, k, missing_list, regX, regY):
        
        # useful parameters
        m = A[0].shape[0]
        ns = [a.shape[1] for a in A]
        if missing_list == None: missing_list = [[]]*len(self.L)

        # initialize A, X, Y
        B = self._initialize_A(A, missing_list)
        X0, Y0 = self._initialize_XY(B, k, missing_list)
        self.X0, self.Y0 = X0, Y0

        # cvxpy problems
        Xv, Yp = cp.Variable(m,k), [cp.Parameter(k+1,ni) for ni in ns]
        Xp, Yv = cp.Parameter(m,k+1), [cp.Variable(k+1,ni) for ni in ns]
        Xp.value = copy(X0)
        for yj, yj0 in zip(Yp, Y0): yj.value = copy(yj0)
        onesM = cp.Constant(ones((m,1)))

        obj = sum(L(Aj, cp.mul_elemwise(mask, Xv*yj[:-1,:] \
                + onesM*yj[-1:,:]) + offset) + ry(yj[:-1,:])\
                for L, Aj, yj, mask, offset, ry in \
                zip(self.L, A, Yp, self.masks, self.offsets, regY)) + regX(Xv)
        pX = cp.Problem(cp.Minimize(obj))
        pY = [cp.Problem(cp.Minimize(\
                L(Aj, cp.mul_elemwise(mask, Xp*yj) + offset) \
                + ry(yj[:-1,:]) + regX(Xp))) \
                for L, Aj, yj, mask, offset, ry in zip(self.L, A, Yv, self.masks, self.offsets, regY)]

        self.probX = (Xv, Yp, pX)
        self.probY = (Xp, Yv, pY) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Constant [as 别名]
def _run_cvx_optimization(self, next_states, rewards, **solver_options):
        """Tensorflow wrapper around a cvxpy value function optimization.

        Parameters
        ----------
        next_states : ndarray
        rewards : ndarray

        Returns
        -------
        values : ndarray
            The optimal values at the states.
        """
        # Define random variables; convert index from np.int64 to regular
        # python int to avoid strange cvxpy error; see:
        # https://github.com/cvxgrp/cvxpy/issues/380
        values = cvxpy.Variable(rewards.shape)

        value_matrix = self.value_function.tri.parameter_derivative(
            next_states)
        # Make cvxpy work with sparse matrices
        value_matrix = cvxpy.Constant(value_matrix)

        objective = cvxpy.Maximize(cvxpy.sum(values))
        constraints = [values <= rewards + self.gamma * value_matrix * values]
        prob = cvxpy.Problem(objective, constraints)

        # Solve optimization problem
        prob.solve(**solver_options)

        # Some error checking
        if not prob.status == cvxpy.OPTIMAL:
            raise OptimizationError('Optimization problem is {}'
                                    .format(prob.status))

        return np.array(values.value) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Constant [as 别名]
def cost(self):
        p = -self.terminals[0].power_var

        segments = [cvx.Constant(self.no_load_cost)]
        prev_power = None
        for power, price in self.bid_curve[1:]:
            if prev_power is None:
                offset = self.no_load_cost
            else:
                offset += (power - prev_power)*prev_price
            segments.append(price*(p - power) + offset)
            prev_power = power
            prev_price = price

        return cvx.max_elemwise(*segments) 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Constant [as 别名]
def admm(self, rho=None, max_iter=50, restarts=5, alpha=1.8,
         random=False, sigma=1.0, gamma=1e6, polish_best=True,
         num_procs=None, parallel=True, seed=1, show_progress=False,
         prox_polished=False, polish_depth=5,
         neighbor_func=None, polish_func=None,
         *args, **kwargs):
    # rho is a list of values, one for each restart.
    if rho is None:
        rho = [np.random.uniform() for i in range(restarts)]
    else:
        assert len(rho) == restarts
    # num_procs is the number of processors to launch.
    if num_procs is None:
        num_procs = multiprocessing.cpu_count()

    # Construct the relaxation.
    if type(self.objective) == cvx.Minimize:
        rel_obj = self.objective
    else:
        rel_obj = -self.objective
    rel_constr = self.constraints
    for var in get_noncvx_vars(self):
        rel_constr += var.relax()
    rel_prob = cvx.Problem(rel_obj, rel_constr)

    # HACK skip this.
    # lower_bound = rel_prob.solve(*args, **kwargs)
    lower_bound = -np.inf
    if show_progress:
        print("lower bound =", lower_bound)

    # Algorithm.
    if parallel:
        pool = multiprocessing.Pool(num_procs)
        tmp_prob = cvx.Problem(rel_prob.objective, rel_prob.constraints)
        best_per_rho = pool.map(admm_inner_iter,
            [(idx, tmp_prob, None, rho_val, gamma, max_iter,
              random, polish_best, seed, sigma, show_progress, neighbor_func, polish_func,
              prox_polished, polish_depth, lower_bound, alpha, args, kwargs) for idx, rho_val in enumerate(rho)])
        pool.close()
        pool.join()
    else:
        xvars = {var.id: var for var in rel_prob.variables()}
        prox = Prox(rel_prob, xvars)
        best_per_rho = list(map(admm_inner_iter,
            [(idx, rel_prob, prox, rho_val, gamma, max_iter,
              random, polish_best, seed, sigma, show_progress, neighbor_func, polish_func,
              prox_polished, polish_depth, lower_bound, alpha, args, kwargs) for idx, rho_val in enumerate(rho)]))
    # Merge best so far.
    argmin = min([(val[0], idx) for idx, val in enumerate(best_per_rho)])[1]
    best_so_far = best_per_rho[argmin]
    #print "best found", best_so_far[0]
    # Unpack result.
    for var in self.variables():
        var.value = best_so_far[1][var.id]

    residual = cvx.Constant(0)
    for constr in self.constraints:
        residual += get_constr_error(constr)

    return self.objective.value, residual.value 

# 需要导入模块: import cvxpy [as 别名]
# 或者: from cvxpy import Constant [as 别名]
def cvxpy_solve_qp(P, q, G=None, h=None, A=None, b=None, initvals=None,
                   solver=None, verbose=False):
    """
    Solve a Quadratic Program defined as:

    .. math::

        \\begin{split}\\begin{array}{ll}
        \\mbox{minimize} &
            \\frac{1}{2} x^T P x + q^T x \\\\
        \\mbox{subject to}
            & G x \\leq h                \\\\
            & A x = h
        \\end{array}\\end{split}

    calling a given solver using the `CVXPY <http://www.cvxpy.org/>`_ modelling
    language.

    Parameters
    ----------
    P : array, shape=(n, n)
        Primal quadratic cost matrix.
    q : array, shape=(n,)
        Primal quadratic cost vector.
    G : array, shape=(m, n)
        Linear inequality constraint matrix.
    h : array, shape=(m,)
        Linear inequality constraint vector.
    A : array, shape=(meq, n), optional
        Linear equality constraint matrix.
    b : array, shape=(meq,), optional
        Linear equality constraint vector.
    initvals : array, shape=(n,), optional
        Warm-start guess vector (not used).
    solver : string, optional
        Solver name in ``cvxpy.installed_solvers()``.
    verbose : bool, optional
        Set to `True` to print out extra information.

    Returns
    -------
    x : array, shape=(n,)
        Solution to the QP, if found, otherwise ``None``.
    """
    if initvals is not None:
        print("CVXPY: note that warm-start values are ignored by wrapper")
    n = q.shape[0]
    x = Variable(n)
    P = Constant(P)  # see http://www.cvxpy.org/en/latest/faq/
    objective = Minimize(0.5 * quad_form(x, P) + q * x)
    constraints = []
    if G is not None:
        constraints.append(G * x <= h)
    if A is not None:
        constraints.append(A * x == b)
    prob = Problem(objective, constraints)
    prob.solve(solver=solver, verbose=verbose)
    x_opt = array(x.value).reshape((n,))
    return x_opt