예제 #1
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    def setUp(self):
        'Test remove constraint'

        model = CyLPModel()

        x = model.addVariable('x', 3)
        y = model.addVariable('y', 2)

        A = csc_matrixPlus(([1, 2, 1, 1], ([0, 0, 1, 1], [0, 1, 0, 2])),  shape=(2, 3))
        B = csc_matrixPlus(([1, 1], ([0, 1], [0, 2])),  shape=(2, 3))
        D = np.matrix([[1., 2.],[0, 1]])
        a = CyLPArray([3, 2.5])
        b = CyLPArray([4.2, 3])
        x_u= CyLPArray([2., 3.5])

        model.addConstraint(A * x <= a, 'res1')
        model.addConstraint(2 <= B * x + D * y <= b, 'res2')
        model.addConstraint(y >= 0)
        model.addConstraint(1.1 <= x[1:3] <= x_u)
        model.addConstraint(x[0] >= 0.1)

        c = CyLPArray([1., -2., 3.])
        model.objective = c * x + 2 * y[0] + 2 * y[1]

        s = CyClpSimplex(model)
        self.s = s
        self.x = x
        self.y = y
예제 #2
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    def test(self):
        model = CyLPModel()

        x = model.addVariable('x', 3)

        A = np.matrix([[1,2,3], [1,1,1]])
        b = CyLPArray([5, 3])

        model.addConstraint(A * x == b)
        model.addConstraint(x >= 0)

        model.objective = 1*x[0]  + 1*x[1] + 1.1 * x[2]

        # Solve it a first time
        s = CyClpSimplex(model)
        s.primal()
        sol = s.primalVariableSolution['x']
        self.assertTrue((abs(sol - np.array([1,2,0]) ) <= 10**-6).all())
        # Add a cut
        s.addConstraint(x[0] >= 1.1)
        s.primal()
        sol = s.primalVariableSolution['x']
        self.assertTrue((abs(sol - np.array([1.1, 1.8, 0.1]) ) <= 10**-6).all())

        # Change the objective function
        c = csr_matrixPlus([[1, 10, 1.1]]).T
        s.objective = c.T * x
        s.primal()
        sol = s.primalVariableSolution['x']
        self.assertTrue((abs(sol - np.array([2, 0, 1]) ) <= 10**-6).all())
    def test(self):
        model = CyLPModel()

        x = model.addVariable('x', 3)

        A = np.matrix([[1, 2, 3], [1, 1, 1]])
        b = CyLPArray([5, 3])

        model.addConstraint(A * x == b)
        model.addConstraint(x >= 0)

        model.objective = 1 * x[0] + 1 * x[1] + 1.1 * x[2]

        # Solve it a first time
        s = CyClpSimplex(model)
        s.primal()
        sol = s.primalVariableSolution['x']
        self.assertTrue((abs(sol - np.array([1, 2, 0])) <= 10**-6).all())
        # Add a cut
        s.addConstraint(x[0] >= 1.1)
        s.primal()
        sol = s.primalVariableSolution['x']
        self.assertTrue((abs(sol - np.array([1.1, 1.8, 0.1])) <= 10**-6).all())

        # Change the objective function
        c = csr_matrixPlus([[1, 10, 1.1]]).T
        s.objective = c.T * x
        s.primal()
        sol = s.primalVariableSolution['x']
        self.assertTrue((abs(sol - np.array([2, 0, 1])) <= 10**-6).all())
예제 #4
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    def setUp(self):
        'Test remove constraint'

        model = CyLPModel()

        x = model.addVariable('x', 3)
        y = model.addVariable('y', 2)

        A = csc_matrixPlus(([1, 2, 1, 1], ([0, 0, 1, 1], [0, 1, 0, 2])),
                           shape=(2, 3))
        B = csc_matrixPlus(([1, 1], ([0, 1], [0, 2])), shape=(2, 3))
        D = np.matrix([[1., 2.], [0, 1]])
        a = CyLPArray([3, 2.5])
        b = CyLPArray([4.2, 3])
        x_u = CyLPArray([2., 3.5])

        model.addConstraint(A * x <= a, 'res1')
        model.addConstraint(2 <= B * x + D * y <= b, 'res2')
        model.addConstraint(y >= 0)
        model.addConstraint(1.1 <= x[1:3] <= x_u)
        model.addConstraint(x[0] >= 0.1)

        c = CyLPArray([1., -2., 3.])
        model.objective = c * x + 2 * y[0] + 2 * y[1]

        s = CyClpSimplex(model)
        self.s = s
        self.x = x
        self.y = y
예제 #5
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    def solve_cylp(self, verbose: bool = True, **kwargs):
        """ Inspired by the cvxpy cbc layer, ty :) """
        from cylp.cy import CyClpSimplex
        from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray

        cons = self.combine_cons()
        n = int(self.next_var_idx - 1)  # Number of variables.

        # Maximize c@x s.t. A@x <= b  (variable bounds done by constraints)
        c = self.objective.rawdense().squeeze()[
            1:n + 1]  # Objective coefficients, no constants.
        A = cons.raw()[:, 1:n + 1]  # Constraint coefficients.
        b = cons[:, 0].rawdense().squeeze() * -1.0  # Constraint constants.

        model = CyLPModel()
        x = model.addVariable("x", n)  # Variables
        model.objective = c

        # I hate this so much. Casting A to a matrix causes A.__mul__ to be
        # be called, which raises NotImplemented, so then x.__rmul__ is called
        # which gets the job done. Using np.matmul or np.dot doesn't
        # trigger x.__rmul__
        # model.addConstraint(np.matrix(A) * x <= CyLPArray(b))  # Works
        model.addConstraint(x.__rmul__(A) <= CyLPArray(b))

        model = CyClpSimplex(model)  # Convert model

        model.logLevel = 0 if not verbose else model.logLevel

        is_integer_model = not np.isclose(self.int_vars.sum(), 0)
        if is_integer_model:
            model.setInteger(x[np.argwhere(self.int_vars)])
            cbcModel = model.getCbcModel()
            cbcModel.solve()
            status = cbcModel.status
            solmodel = cbcModel
        else:
            status = model.initialSolve()
            solmodel = model

        sol_x = np.hstack(  # Pad back to original shape
            (
                [1.0],  # Special constant 1.0 variable.
                solmodel.primalVariableSolution["x"],  # Actual solution
                np.zeros(self.max_vars - n - 1),  # Unused vars
            ))

        solution = {
            "status": status,
            "primal": sol_x,
            "value": solmodel.objectiveValue
        }
        return solution, solution["primal"]
예제 #6
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    def test_Sparse(self):
        model = CyLPModel()

        x = model.addVariable('x', 3)
        y = model.addVariable('y', 2)

        A = csc_matrixPlus(([1, 2, 1, 1], ([0, 0, 1, 1], [0, 1, 0, 2])),  shape=(2, 3))
        B = csc_matrixPlus(([1, 1], ([0, 1], [0, 2])),  shape=(2, 3))
        D = np.matrix([[1., 2.],[0, 1]])
        a = CyLPArray([5, 2.5])
        b = CyLPArray([4.2, 3])
        x_u= CyLPArray([2., 3.5])

        model.addConstraint(A*x <= a)
        model.addConstraint(2 <= B * x + D * y <= b)
        model.addConstraint(y >= 0)
        model.addConstraint(1.1 <= x[1:3] <= x_u)

        c = CyLPArray([1., -2., 3.])
        model.objective = c * x + 2 * y[0] + 2 * y[1]


        s = CyClpSimplex(model)

        s.primal()
        sol = np.concatenate((s.primalVariableSolution['x'],
                              s.primalVariableSolution['y']))
        self.assertTrue((abs(sol -
                        np.array([0.2, 2, 1.1, 0, 0.9]) ) <= 10**-6).all())
    def test_Sparse(self):
        model = CyLPModel()

        x = model.addVariable('x', 3)
        y = model.addVariable('y', 2)

        A = csc_matrixPlus(([1, 2, 1, 1], ([0, 0, 1, 1], [0, 1, 0, 2])),
                           shape=(2, 3))
        B = csc_matrixPlus(([1, 1], ([0, 1], [0, 2])), shape=(2, 3))
        D = np.matrix([[1., 2.], [0, 1]])
        a = CyLPArray([5, 2.5])
        b = CyLPArray([4.2, 3])
        x_u = CyLPArray([2., 3.5])

        model.addConstraint(A * x <= a)
        model.addConstraint(2 <= B * x + D * y <= b)
        model.addConstraint(y >= 0)
        model.addConstraint(1.1 <= x[1:3] <= x_u)

        c = CyLPArray([1., -2., 3.])
        model.objective = c * x + 2 * y[0] + 2 * y[1]

        s = CyClpSimplex(model)

        s.primal()
        sol = np.concatenate(
            (s.primalVariableSolution['x'], s.primalVariableSolution['y']))
        self.assertTrue(
            (abs(sol - np.array([0.2, 2, 1.1, 0, 0.9])) <= 10**-6).all())
예제 #8
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def LP_solver_cylp(A_Matrix, B_vectors, weights, really_verbose=False):
    """
    Solve the Linear Programming problem given in Giangrande et al, 2012 using
    the CyLP module.

    Parameters
    ----------
    A_Matrix : matrix
        Row augmented A matrix, see :py:func:`construct_A_matrix`
    B_vectors : matrix
        Matrix containing B vectors, see :py:func:`construct_B_vectors`
    weights : array
        Weights.
    really_verbose : bool
        True to print CLP messaging. False to suppress.

    Returns
    -------
    soln : array
        Solution to LP problem.

    See Also
    --------
    LP_solver_cvxopt : Solve LP problem using the CVXOPT module.
    LP_solver_pyglpk : Solve LP problem using the PyGLPK module.

    """
    from cylp.cy.CyClpSimplex import CyClpSimplex
    from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray

    n_gates = weights.shape[1] // 2
    n_rays = B_vectors.shape[0]
    soln = np.zeros([n_rays, n_gates])

    # Create CyLPModel and initialize it
    model = CyLPModel()
    G = np.matrix(A_Matrix)
    h = CyLPArray(np.empty(B_vectors.shape[1]))
    x = model.addVariable('x', G.shape[1])
    model.addConstraint(G * x >= h)
    #c = CyLPArray(np.empty(weights.shape[1]))
    c = CyLPArray(np.squeeze(weights[0]))
    model.objective = c * x

    # import model in solver
    s = CyClpSimplex(model)
    # disable logging
    if not really_verbose:
            s.logLevel = 0

    for raynum in range(n_rays):

        # set new B_vector values for actual ray
        s.setRowLowerArray(np.squeeze(np.asarray(B_vectors[raynum])))
        # set new weights (objectives) for actual ray
        #s.setObjectiveArray(np.squeeze(np.asarray(weights[raynum])))
        # solve with dual method, it is faster
        s.dual()
        # extract primal solution
        soln[raynum, :] = s.primalVariableSolution['x'][n_gates: 2 * n_gates]

    # apply smoothing filter on a per scan basis
    soln = smooth_and_trim_scan(soln, window_len=5, window='sg_smooth')
    return soln
예제 #9
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def LP_solver_cylp_mp(A_Matrix, B_vectors, weights, really_verbose=False,
                      proc=1):
    """
    Solve the Linear Programming problem given in Giangrande et al, 2012 using
    the CyLP module using multiple processes.

    Parameters
    ----------
    A_Matrix : matrix
        Row augmented A matrix, see :py:func:`construct_A_matrix`
    B_vectors : matrix
        Matrix containing B vectors, see :py:func:`construct_B_vectors`
    weights : array
        Weights.
    really_verbose : bool
        True to print CLP messaging. False to suppress.
    proc : int
        Number of worker processes.

    Returns
    -------
    soln : array
        Solution to LP problem.

    See Also
    --------
    LP_solver_cvxopt : Solve LP problem using the CVXOPT module.
    LP_solver_pyglpk : Solve LP problem using the PyGLPK module.
    LP_solver_cylp : Solve LP problem using the CyLP module using single
                     process.

    """
    from cylp.cy.CyClpSimplex import CyClpSimplex
    from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray
    import multiprocessing as mp

    n_gates = weights.shape[1] // 2
    n_rays = B_vectors.shape[0]
    soln = np.zeros([n_rays, n_gates])

    # Create CyLPModel and initialize it
    model = CyLPModel()
    G = np.matrix(A_Matrix)
    h = CyLPArray(np.empty(B_vectors.shape[1]))
    x = model.addVariable('x', G.shape[1])
    model.addConstraint(G * x >= h)
    c = CyLPArray(np.empty(weights.shape[1]))
    #c = CyLPArray(np.squeeze(weights[0]))
    model.objective = c * x

    chunksize = int(n_rays/proc)
    # check if equal sized chunks can be distributed to worker processes
    if n_rays % chunksize != 0:
        print("Problem of %d rays cannot be split to %d worker processes!\n\r"
              "Fallback to 1 process!" % (n_rays, proc))
        chunksize = n_rays  # fall back to one process
        proc = 1

    print("Calculating with %d processes, %d rays per chunk" %
          (proc, chunksize))

    def worker(model, B_vectors, weights, ray, chunksize, out_q):
        """
        The worker function, invoked in a process.
        The results are placed in a dictionary that's pushed to a queue.
        """
        outdict = {}
        iray = int(ray/chunksize)
        outdict[iray] = solve_cylp(model, B_vectors, weights, ray, chunksize)
        out_q.put(outdict)

    # Queue for LP solutions
    out_q = mp.Queue()
    procs = []

    # fire off worker processes
    for raynum in range(0, n_rays, chunksize):
        p = mp.Process(target=worker, args=(
            model, B_vectors, weights, raynum, chunksize, out_q))
        procs.append(p)
        p.start()

    # collecting results
    resultdict = {}
    for raynum in range(0, n_rays, chunksize):
        resultdict.update(out_q.get())

    # Wait for all worker processes to finish
    for p in procs:
        p.join()

    # copy results in output array
    for raynum in range(0, int(n_rays / chunksize)):
        soln[raynum * chunksize:raynum * chunksize + chunksize, :] = (
            resultdict[raynum])

    # apply smoothing filter to output array
    soln = smooth_and_trim_scan(soln, window_len=5, window='sg_smooth')

    return soln
예제 #10
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def LP_solver_cylp(A_Matrix, B_vectors, weights, really_verbose=False):
    """
    Solve the Linear Programming problem given in Giangrande et al, 2012 using
    the CyLP module.

    Parameters
    ----------
    A_Matrix : matrix
        Row augmented A matrix, see :py:func:`construct_A_matrix`
    B_vectors : matrix
        Matrix containing B vectors, see :py:func:`construct_B_vectors`
    weights : array
        Weights.
    really_verbose : bool
        True to print CLP messaging. False to suppress.

    Returns
    -------
    soln : array
        Solution to LP problem.

    See Also
    --------
    LP_solver_cvxopt : Solve LP problem using the CVXOPT module.
    LP_solver_pyglpk : Solve LP problem using the PyGLPK module.

    """
    from cylp.cy.CyClpSimplex import CyClpSimplex
    from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray

    n_gates = weights.shape[1] // 2
    n_rays = B_vectors.shape[0]
    soln = np.zeros([n_rays, n_gates])

    # Create CyLPModel and initialize it
    model = CyLPModel()
    G = np.matrix(A_Matrix)
    h = CyLPArray(np.empty(B_vectors.shape[1]))
    x = model.addVariable('x', G.shape[1])
    model.addConstraint(G * x >= h)
    #c = CyLPArray(np.empty(weights.shape[1]))
    c = CyLPArray(np.squeeze(weights[0]))
    model.objective = c * x

    # import model in solver
    s = CyClpSimplex(model)
    # disable logging
    if not really_verbose:
        s.logLevel = 0

    for raynum in range(n_rays):

        # set new B_vector values for actual ray
        s.setRowLowerArray(np.squeeze(np.asarray(B_vectors[raynum])))
        # set new weights (objectives) for actual ray
        #s.setObjectiveArray(np.squeeze(np.asarray(weights[raynum])))
        # solve with dual method, it is faster
        s.dual()
        # extract primal solution
        soln[raynum, :] = s.primalVariableSolution['x'][n_gates:2 * n_gates]

    # apply smoothing filter on a per scan basis
    soln = smooth_and_trim_scan(soln, window_len=5, window='sg_smooth')
    return soln
예제 #11
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def LP_solver_cylp_mp(A_Matrix,
                      B_vectors,
                      weights,
                      really_verbose=False,
                      proc=1):
    """
    Solve the Linear Programming problem given in Giangrande et al, 2012 using
    the CyLP module using multiple processes.

    Parameters
    ----------
    A_Matrix : matrix
        Row augmented A matrix, see :py:func:`construct_A_matrix`
    B_vectors : matrix
        Matrix containing B vectors, see :py:func:`construct_B_vectors`
    weights : array
        Weights.
    really_verbose : bool
        True to print CLP messaging. False to suppress.
    proc : int
        Number of worker processes.

    Returns
    -------
    soln : array
        Solution to LP problem.

    See Also
    --------
    LP_solver_cvxopt : Solve LP problem using the CVXOPT module.
    LP_solver_pyglpk : Solve LP problem using the PyGLPK module.
    LP_solver_cylp : Solve LP problem using the CyLP module using single
                     process.

    """
    from cylp.cy.CyClpSimplex import CyClpSimplex
    from cylp.py.modeling.CyLPModel import CyLPModel, CyLPArray
    import multiprocessing as mp

    n_gates = weights.shape[1] // 2
    n_rays = B_vectors.shape[0]
    soln = np.zeros([n_rays, n_gates])

    # Create CyLPModel and initialize it
    model = CyLPModel()
    G = np.matrix(A_Matrix)
    h = CyLPArray(np.empty(B_vectors.shape[1]))
    x = model.addVariable('x', G.shape[1])
    model.addConstraint(G * x >= h)
    c = CyLPArray(np.empty(weights.shape[1]))
    #c = CyLPArray(np.squeeze(weights[0]))
    model.objective = c * x

    chunksize = int(n_rays / proc)
    # check if equal sized chunks can be distributed to worker processes
    if n_rays % chunksize != 0:
        print("Problem of %d rays cannot be split to %d worker processes!\n\r"
              "Fallback to 1 process!" % (n_rays, proc))
        chunksize = n_rays  # fall back to one process
        proc = 1

    print("Calculating with %d processes, %d rays per chunk" %
          (proc, chunksize))

    def worker(model, B_vectors, weights, ray, chunksize, out_q):
        """
        The worker function, invoked in a process.
        The results are placed in a dictionary that's pushed to a queue.
        """
        outdict = {}
        iray = int(ray / chunksize)
        outdict[iray] = solve_cylp(model, B_vectors, weights, ray, chunksize)
        out_q.put(outdict)

    # Queue for LP solutions
    out_q = mp.Queue()
    procs = []

    # fire off worker processes
    for raynum in range(0, n_rays, chunksize):
        p = mp.Process(target=worker,
                       args=(model, B_vectors, weights, raynum, chunksize,
                             out_q))
        procs.append(p)
        p.start()

    # collecting results
    resultdict = {}
    for raynum in range(0, n_rays, chunksize):
        resultdict.update(out_q.get())

    # Wait for all worker processes to finish
    for p in procs:
        p.join()

    # copy results in output array
    for raynum in range(0, int(n_rays / chunksize)):
        soln[raynum * chunksize:raynum * chunksize +
             chunksize, :] = (resultdict[raynum])

    # apply smoothing filter to output array
    soln = smooth_and_trim_scan(soln, window_len=5, window='sg_smooth')

    return soln
예제 #12
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def solve_lp_for_year(puf, Stage_I_factors, Stage_II_targets, year, tol):

    puf_length = len(puf.s006)

    print("Preparing coefficient matrix for year {} .....".format(year))

    s006 = np.where(puf.e02400 > 0,
                    puf.s006 * Stage_I_factors[year]["APOPSNR"] / 100,
                    puf.s006 * Stage_I_factors[year]["ARETS"] / 100)

    single_return = np.where((puf.mars == 1) & (puf.filer == 1), s006, 0)
    joint_return = np.where(
        ((puf.mars == 2) | (puf.mars == 3)) & (puf.filer == 1), s006, 0)
    hh_return = np.where((puf.mars == 4) & (puf.filer == 1), s006, 0)
    return_w_SS = np.where((puf.e02400 > 0) & (puf.filer == 1), s006, 0)

    dependent_exempt_num = (puf.xocah + puf.xocawh + puf.xoodep +
                            puf.xopar) * s006
    interest = puf.e00300 * s006
    dividend = puf.e00600 * s006
    biz_income = np.where(puf.e00900 > 0, puf.e00900, 0) * s006
    biz_loss = np.where(puf.e00900 < 0, -puf.e00900, 0) * s006
    cap_gain = np.where(
        (puf.p23250 + puf.p22250) > 0, puf.p23250 + puf.p22250, 0) * s006
    annuity_pension = puf.e01700 * s006
    sch_e_income = np.where(puf.e02000 > 0, puf.e02000, 0) * s006
    sch_e_loss = np.where(puf.e02000 < 0, -puf.e02000, 0) * s006
    ss_income = np.where(puf.filer == 1, puf.e02400, 0) * s006
    unemployment_comp = puf.e02300 * s006

    # Wage distribution
    wage_1 = np.where(puf.e00100 <= 0, puf.e00200, 0) * s006
    wage_2 = np.where(
        (puf.e00100 > 0) & (puf.e00100 <= 10000), puf.e00200, 0) * s006
    wage_3 = np.where(
        (puf.e00100 > 10000) & (puf.e00100 <= 20000), puf.e00200, 0) * s006
    wage_4 = np.where(
        (puf.e00100 > 20000) & (puf.e00100 <= 30000), puf.e00200, 0) * s006
    wage_5 = np.where(
        (puf.e00100 > 30000) & (puf.e00100 <= 40000), puf.e00200, 0) * s006
    wage_6 = np.where(
        (puf.e00100 > 40000) & (puf.e00100 <= 50000), puf.e00200, 0) * s006
    wage_7 = np.where(
        (puf.e00100 > 50000) & (puf.e00100 <= 75000), puf.e00200, 0) * s006
    wage_8 = np.where(
        (puf.e00100 > 75000) & (puf.e00100 <= 100000), puf.e00200, 0) * s006
    wage_9 = np.where(
        (puf.e00100 > 100000) & (puf.e00100 <= 200000), puf.e00200, 0) * s006
    wage_10 = np.where(
        (puf.e00100 > 200000) & (puf.e00100 <= 500000), puf.e00200, 0) * s006
    wage_11 = np.where(
        (puf.e00100 > 500000) & (puf.e00100 <= 1000000), puf.e00200, 0) * s006
    wage_12 = np.where(puf.e00100 > 1000000, puf.e00200, 0) * s006

    # Set up the matrix
    One_half_LHS = np.vstack(
        (single_return, joint_return, hh_return, return_w_SS,
         dependent_exempt_num, interest, dividend, biz_income, biz_loss,
         cap_gain, annuity_pension, sch_e_income, sch_e_loss, ss_income,
         unemployment_comp, wage_1, wage_2, wage_3, wage_4, wage_5, wage_6,
         wage_7, wage_8, wage_9, wage_10, wage_11, wage_12))

    # Coefficients for r and s
    A1 = np.matrix(One_half_LHS)
    A2 = np.matrix(-One_half_LHS)

    print("Preparing targets for year {} .....".format(year))

    APOPN = Stage_I_factors[year]["APOPN"]

    b = []

    b.append(Stage_II_targets[year]["Single Returns"] - single_return.sum())
    b.append(Stage_II_targets[year]["Joint Returns"] - joint_return.sum())
    target_name = "Head of Household Returns"
    b.append(Stage_II_targets[year][target_name] - hh_return.sum())
    target_name = "Number of Returns w/ Gross Security Income"
    b.append(Stage_II_targets[year][target_name] - return_w_SS.sum())
    target_name = "Number of Dependent Exemptions"
    b.append(Stage_II_targets[year][target_name] - dependent_exempt_num.sum())

    AINTS = Stage_I_factors[year]["AINTS"]
    INTEREST = (Stage_II_targets[year]["Taxable Interest Income"] * APOPN /
                AINTS * 1000 - interest.sum())

    ADIVS = Stage_I_factors[year]["ADIVS"]
    DIVIDEND = (
        Stage_II_targets[year]["Ordinary Dividends"] * APOPN / ADIVS * 1000 -
        dividend.sum())

    ASCHCI = Stage_I_factors[year]["ASCHCI"]
    BIZ_INCOME = (Stage_II_targets[year]["Business Income (Schedule C)"] *
                  APOPN / ASCHCI * 1000 - biz_income.sum())

    ASCHCL = Stage_I_factors[year]["ASCHCL"]
    BIZ_LOSS = (Stage_II_targets[year]["Business Loss (Schedule C)"] * APOPN /
                ASCHCL * 1000 - biz_loss.sum())

    ACGNS = Stage_I_factors[year]["ACGNS"]
    CAP_GAIN = (Stage_II_targets[year]["Net Capital Gains in AGI"] * APOPN /
                ACGNS * 1000 - cap_gain.sum())

    ATXPY = Stage_I_factors[year]["ATXPY"]
    target_name = "Taxable Pensions and Annuities"
    ANNUITY_PENSION = (
        Stage_II_targets[year][target_name] * APOPN / ATXPY * 1000 -
        annuity_pension.sum())

    ASCHEI = Stage_I_factors[year]["ASCHEI"]
    target_name = "Supplemental Income (Schedule E)"
    SCH_E_INCOME = (
        Stage_II_targets[year][target_name] * APOPN / ASCHEI * 1000 -
        sch_e_income.sum())

    ASCHEL = Stage_I_factors[year]["ASCHEL"]
    SCH_E_LOSS = (Stage_II_targets[year]["Supplemental Loss (Schedule E)"] *
                  APOPN / ASCHEL * 1000 - sch_e_loss.sum())

    ASOCSEC = Stage_I_factors[year]["ASOCSEC"]
    APOPSNR = Stage_I_factors[year]["APOPSNR"]
    SS_INCOME = (Stage_II_targets[year]["Gross Social Security Income"] *
                 APOPSNR / ASOCSEC * 1000 - ss_income.sum())

    AUCOMP = Stage_I_factors[year]["AUCOMP"]
    UNEMPLOYMENT_COMP = (Stage_II_targets[year]["Unemployment Compensation"] *
                         APOPN / AUCOMP * 1000 - unemployment_comp.sum())

    AWAGE = Stage_I_factors[year]["AWAGE"]
    target_name = "Wages and Salaries: Zero or Less"
    WAGE_1 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
              wage_1.sum())
    target_name = "Wages and Salaries: $1 Less Than $10,000"
    WAGE_2 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
              wage_2.sum())
    target_name = "Wages and Salaries: $10,000 Less Than $20,000"
    WAGE_3 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
              wage_3.sum())
    target_name = "Wages and Salaries: $20,000 Less Than $30,000"
    WAGE_4 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
              wage_4.sum())
    target_name = "Wages and Salaries: $30,000 Less Than $40,000"
    WAGE_5 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
              wage_5.sum())
    target_name = "Wages and Salaries: $40,000 Less Than $50,000"
    WAGE_6 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
              wage_6.sum())
    target_name = "Wages and Salaries: $50,000 Less Than $75,000"
    WAGE_7 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
              wage_7.sum())
    target_name = "Wages and Salaries: $75,000 Less Than $100,000"
    WAGE_8 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
              wage_8.sum())
    target_name = "Wages and Salaries: $100,000 Less Than $200,000"
    WAGE_9 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
              wage_9.sum())
    target_name = "Wages and Salaries: $200,000 Less Than $500,000"
    WAGE_10 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
               wage_10.sum())
    target_name = "Wages and Salaries: $500,000 Less Than $1 Million"
    WAGE_11 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
               wage_11.sum())
    target_name = "Wages and Salaries: $1 Million and Over"
    WAGE_12 = (Stage_II_targets[year][target_name] * APOPN / AWAGE * 1000 -
               wage_12.sum())

    temp = [
        INTEREST, DIVIDEND, BIZ_INCOME, BIZ_LOSS, CAP_GAIN, ANNUITY_PENSION,
        SCH_E_INCOME, SCH_E_LOSS, SS_INCOME, UNEMPLOYMENT_COMP, WAGE_1, WAGE_2,
        WAGE_3, WAGE_4, WAGE_5, WAGE_6, WAGE_7, WAGE_8, WAGE_9, WAGE_10,
        WAGE_11, WAGE_12
    ]
    for m in temp:
        b.append(m)

    targets = CyLPArray(b)
    print("Targets for year {} are:".format(year))
    print(targets)

    LP = CyLPModel()

    r = LP.addVariable("r", puf_length)
    s = LP.addVariable("s", puf_length)

    print("Adding constraints for year {} .....".format(year))
    LP.addConstraint(r >= 0, "positive r")
    LP.addConstraint(s >= 0, "positive s")
    LP.addConstraint(r + s <= tol, "abs upperbound")

    c = CyLPArray((np.ones(puf_length)))
    LP.objective = c * r + c * s

    LP.addConstraint(A1 * r + A2 * s == targets, "Aggregates")

    print("Setting up the LP model for year {} .....".format(year))
    model = CyClpSimplex(LP)

    print("Solving LP for year {} .....".format(year))
    model.initialSolve()

    print("DONE solving LP for year {}".format(year))
    z = np.empty([puf_length])
    z = (1.0 + model.primalVariableSolution["r"] -
         model.primalVariableSolution["s"]) * s006 * 100
    return z
예제 #13
0
def Single_Year_Stage_II(puf, Stage_I_factors, Stage_II_targets, year, tol):


    length = len(puf.s006)


    print("Preparing coefficient matrix...")

    s006 = np.where(puf.e02400>0,
                    puf.s006*Stage_I_factors[year]["APOPSNR"]/100,
                    puf.s006*Stage_I_factors[year]["ARETS"]/100)



    single_return = np.where(puf.mars==1, s006, 0)
    joint_return = np.where((puf.mars==2)|(puf.mars==3), s006, 0)
    hh_return = np.where(puf.mars==4,s006,0)
    return_w_SS = np.where(puf.e02400>0,s006,0)

    dependent_exempt_num = (puf.xocah+puf.xocawh+puf.xoodep+puf.xopar)*s006
    interest = puf.e00300*s006
    dividend = puf.e00600*s006
    biz_income = np.where(puf.e00900>0, puf.e00900, 0)*s006
    biz_loss = np.where(puf.e00900<0, -puf.e00900, 0)*s006
    cap_gain = np.where(puf.e01000>0, puf.e01000, 0)*s006
    annuity_pension = puf.e01700*s006
    sch_e_income = np.where(puf.e02000>0, puf.e02000, 0)*s006
    sch_e_loss = np.where(puf.e02000<0, -puf.e02000, 0)*s006
    ss_income = puf.e02400*s006
    unemployment_comp = puf.e02300*s006


    # Wage distribution

    wage_1 = np.where(puf.e00100<=0, puf.e00200,0)*s006
    wage_2 = np.where((puf.e00100>0)&(puf.e00100<=10000), puf.e00200,0)*s006
    wage_3 = np.where((puf.e00100>10000)&(puf.e00100<=20000), puf.e00200,0)*s006
    wage_4 = np.where((puf.e00100>20000)&(puf.e00100<=30000), puf.e00200,0)*s006
    wage_5 = np.where((puf.e00100>30000)&(puf.e00100<=40000), puf.e00200,0)*s006
    wage_6 = np.where((puf.e00100>40000)&(puf.e00100<=50000), puf.e00200,0)*s006
    wage_7 = np.where((puf.e00100>50000)&(puf.e00100<=75000), puf.e00200,0)*s006
    wage_8 = np.where((puf.e00100>75000)&(puf.e00100<=100000), puf.e00200,0)*s006
    wage_9 = np.where((puf.e00100>100000)&(puf.e00100<=200000), puf.e00200,0)*s006
    wage_10 = np.where((puf.e00100>200000)&(puf.e00100<=500000), puf.e00200,0)*s006
    wage_11 = np.where((puf.e00100>500000)&(puf.e00100<=1000000), puf.e00200,0)*s006
    wage_12 = np.where((puf.e00100>1000000), puf.e00200,0)*s006


    # Set up the matrix
    One_half_LHS = np.vstack((single_return, joint_return, hh_return, return_w_SS,
                              dependent_exempt_num, interest, dividend,
                              biz_income,biz_loss, cap_gain, annuity_pension,
                              sch_e_income, sch_e_loss, ss_income, unemployment_comp,
                              wage_1, wage_2, wage_3, wage_4, wage_5, wage_6,
                              wage_7, wage_8, wage_9, wage_10, wage_11, wage_12))


    # Coefficients for r and s
    A1 = np.matrix(One_half_LHS)
    A2 = np.matrix(-One_half_LHS)


    print("Preparing targets for ", year)

    APOPN = Stage_I_factors[year]["APOPN"]

    b = []

    b.append(Stage_II_targets[year]['Single']-single_return.sum())
    b.append(Stage_II_targets[year]['Joint']-joint_return.sum())
    b.append(Stage_II_targets[year]['HH']-hh_return.sum())
    b.append(Stage_II_targets[year]['SS_return']-return_w_SS.sum())

    b.append(Stage_II_targets[year]['Dep_return'] -  dependent_exempt_num.sum())

    AINTS = Stage_I_factors[year]["AINTS"]
    INTEREST = Stage_II_targets[year]['INTS']*APOPN/AINTS*1000-interest.sum()

    ADIVS = Stage_I_factors[year]["ADIVS"]
    DIVIDEND = Stage_II_targets[year]['DIVS']*APOPN/ADIVS*1000 - dividend.sum()

    ASCHCI = Stage_I_factors[year]["ASCHCI"]
    BIZ_INCOME = Stage_II_targets[year]['SCHCI']*APOPN/ASCHCI*1000 - biz_income.sum()


    ASCHCL = Stage_I_factors[year]["ASCHCL"]
    BIZ_LOSS = Stage_II_targets[year]['SCHCL']*APOPN/ASCHCL*1000 - biz_loss.sum()

    ACGNS = Stage_I_factors[year]["ACGNS"]
    CAP_GAIN = Stage_II_targets[year]['CGNS']*APOPN/ACGNS*1000 - cap_gain.sum()

    ATXPY = Stage_I_factors[year]["ATXPY"]
    ANNUITY_PENSION = Stage_II_targets[year]['Pension']*APOPN/ATXPY*1000 - annuity_pension.sum()

    ASCHEI = Stage_I_factors[year]["ASCHEI"]
    SCH_E_INCOME = Stage_II_targets[year]["SCHEI"]*APOPN/ASCHEI*1000 - sch_e_income.sum()

    ASCHEL = Stage_I_factors[year]["ASCHEL"]
    SCH_E_LOSS = Stage_II_targets[year]["SCHEL"]*APOPN/ASCHEL*1000 - sch_e_loss.sum()

    ASOCSEC = Stage_I_factors[year]["ASOCSEC"]
    APOPSNR = Stage_I_factors[year]["APOPSNR"]
    SS_INCOME = Stage_II_targets[year]["SS"]*APOPSNR/ASOCSEC*1000 - ss_income.sum()

    AUCOMP = Stage_I_factors[year]["AUCOMP"]
    UNEMPLOYMENT_COMP = Stage_II_targets[year]["UCOMP"]*APOPN/AUCOMP*1000 - unemployment_comp.sum()

    AWAGE = Stage_I_factors[year]["AWAGE"]
    WAGE_1 = Stage_II_targets[year]["WAGE_1"]*APOPN/AWAGE*1000 - wage_1.sum()
    WAGE_2 = Stage_II_targets[year]["WAGE_2"]*APOPN/AWAGE*1000 - wage_2.sum()
    WAGE_3 = Stage_II_targets[year]["WAGE_3"]*APOPN/AWAGE*1000 - wage_3.sum()
    WAGE_4 = Stage_II_targets[year]["WAGE_4"]*APOPN/AWAGE*1000 - wage_4.sum()
    WAGE_5 = Stage_II_targets[year]["WAGE_5"]*APOPN/AWAGE*1000 - wage_5.sum()
    WAGE_6 = Stage_II_targets[year]["WAGE_6"]*APOPN/AWAGE*1000 - wage_6.sum()
    WAGE_7 = Stage_II_targets[year]["WAGE_7"]*APOPN/AWAGE*1000 - wage_7.sum()
    WAGE_8 = Stage_II_targets[year]["WAGE_8"]*APOPN/AWAGE*1000 - wage_8.sum()
    WAGE_9 = Stage_II_targets[year]["WAGE_9"]*APOPN/AWAGE*1000 - wage_9.sum()
    WAGE_10 = Stage_II_targets[year]["WAGE_10"]*APOPN/AWAGE*1000 - wage_10.sum()
    WAGE_11 = Stage_II_targets[year]["WAGE_11"]*APOPN/AWAGE*1000 - wage_11.sum()
    WAGE_12 = Stage_II_targets[year]["WAGE_12"]*APOPN/AWAGE*1000 - wage_12.sum()



    temp = [INTEREST,DIVIDEND, BIZ_INCOME, BIZ_LOSS, CAP_GAIN, ANNUITY_PENSION, SCH_E_INCOME, SCH_E_LOSS, SS_INCOME, UNEMPLOYMENT_COMP,
            WAGE_1,WAGE_2, WAGE_3,WAGE_4, WAGE_5, WAGE_6, WAGE_7,WAGE_8,WAGE_9, WAGE_10, WAGE_11, WAGE_12]
    for m in temp:
        b.append(m)

    targets = CyLPArray(b)
    print("Targets for year ", year, " is ", targets)

    LP = CyLPModel()

    r = LP.addVariable('r', length)
    s = LP.addVariable('s', length)

    print("Adding constraints")
    LP.addConstraint(r >=0, "positive r")
    LP.addConstraint(s >=0, "positive s")
    LP.addConstraint(r + s <= tol, "abs upperbound")

    c = CyLPArray((np.ones(length)))
    LP.objective = c * r + c * s




    LP.addConstraint(A1 * r + A2 * s == targets, "Aggregates")

    print("Setting up the LP model")
    model = CyClpSimplex(LP)


    print("Solving LP......")
    model.initialSolve()

    print("DONE!!")
    z = np.empty([length])
    z = (1+model.primalVariableSolution['r'] - model.primalVariableSolution['s'])*s006
    return z
예제 #14
0
def Single_Year_Stage_II(puf, Stage_I_factors, Stage_II_targets, year, tol):


    length = len(puf.s006)


    print("Preparing coefficient matrix...")

    s006 = np.where(puf.e02400>0,
                    puf.s006*Stage_I_factors[year]["APOPSNR"]/100,
                    puf.s006*Stage_I_factors[year]["ARETS"]/100)



    single_return = np.where((puf.mars==1) & (puf.filer==1), s006, 0)
    joint_return = np.where(((puf.mars==2)|(puf.mars==3)) & (puf.filer==1), s006, 0)
    hh_return = np.where((puf.mars==4) & (puf.filer==1),s006,0)
    return_w_SS = np.where((puf.e02400>0) & (puf.filer==1),s006,0)

    dependent_exempt_num = (puf.xocah+puf.xocawh+puf.xoodep+puf.xopar)*s006
    interest = puf.e00300*s006
    dividend = puf.e00600*s006
    biz_income = np.where(puf.e00900>0, puf.e00900, 0)*s006
    biz_loss = np.where(puf.e00900<0, -puf.e00900, 0)*s006
    cap_gain = np.where((puf.p23250+puf.p22250)>0, (puf.p23250+puf.p22250), 0)*s006
    annuity_pension = puf.e01700*s006
    sch_e_income = np.where(puf.e02000>0, puf.e02000, 0)*s006
    sch_e_loss = np.where(puf.e02000<0, -puf.e02000, 0)*s006
    ss_income = puf.e02400*s006
    unemployment_comp = puf.e02300*s006


    # Wage distribution

    wage_1 = np.where(puf.e00100<=0, puf.e00200,0)*s006
    wage_2 = np.where((puf.e00100>0)&(puf.e00100<=10000), puf.e00200,0)*s006
    wage_3 = np.where((puf.e00100>10000)&(puf.e00100<=20000), puf.e00200,0)*s006
    wage_4 = np.where((puf.e00100>20000)&(puf.e00100<=30000), puf.e00200,0)*s006
    wage_5 = np.where((puf.e00100>30000)&(puf.e00100<=40000), puf.e00200,0)*s006
    wage_6 = np.where((puf.e00100>40000)&(puf.e00100<=50000), puf.e00200,0)*s006
    wage_7 = np.where((puf.e00100>50000)&(puf.e00100<=75000), puf.e00200,0)*s006
    wage_8 = np.where((puf.e00100>75000)&(puf.e00100<=100000), puf.e00200,0)*s006
    wage_9 = np.where((puf.e00100>100000)&(puf.e00100<=200000), puf.e00200,0)*s006
    wage_10 = np.where((puf.e00100>200000)&(puf.e00100<=500000), puf.e00200,0)*s006
    wage_11 = np.where((puf.e00100>500000)&(puf.e00100<=1000000), puf.e00200,0)*s006
    wage_12 = np.where((puf.e00100>1000000), puf.e00200,0)*s006


    # Set up the matrix
    One_half_LHS = np.vstack((single_return, joint_return, hh_return, return_w_SS,
                              dependent_exempt_num, interest, dividend,
                              biz_income,biz_loss, cap_gain, annuity_pension,
                              sch_e_income, sch_e_loss, ss_income, unemployment_comp,
                              wage_1, wage_2, wage_3, wage_4, wage_5, wage_6,
                              wage_7, wage_8, wage_9, wage_10, wage_11, wage_12))


    # Coefficients for r and s
    A1 = np.matrix(One_half_LHS)
    A2 = np.matrix(-One_half_LHS)


    print("Preparing targets for ", year)

    APOPN = Stage_I_factors[year]["APOPN"]

    b = []

    b.append(Stage_II_targets[year]['Single Returns']-single_return.sum())
    b.append(Stage_II_targets[year]['Joint Returns']-joint_return.sum())
    b.append(Stage_II_targets[year]['Head of Household Returns']-hh_return.sum())
    b.append(Stage_II_targets[year]['Number of Returns w/ Gross Security Income']-return_w_SS.sum())

    b.append(Stage_II_targets[year]['Number of Dependent Exemptions'] -  dependent_exempt_num.sum())

    AINTS = Stage_I_factors[year]["AINTS"]
    INTEREST = Stage_II_targets[year]['Taxable Interest Income']*APOPN/AINTS*1000-interest.sum()

    ADIVS = Stage_I_factors[year]["ADIVS"]
    DIVIDEND = Stage_II_targets[year]['Ordinary Dividends']*APOPN/ADIVS*1000 - dividend.sum()

    ASCHCI = Stage_I_factors[year]["ASCHCI"]
    BIZ_INCOME = Stage_II_targets[year]['Business Income (Schedule C)']*APOPN/ASCHCI*1000 - biz_income.sum()


    ASCHCL = Stage_I_factors[year]["ASCHCL"]
    BIZ_LOSS = Stage_II_targets[year]['Business Loss (Schedule C)']*APOPN/ASCHCL*1000 - biz_loss.sum()

    ACGNS = Stage_I_factors[year]["ACGNS"]
    CAP_GAIN = Stage_II_targets[year]['Net Capital Gains in AGI']*APOPN/ACGNS*1000 - cap_gain.sum()

    ATXPY = Stage_I_factors[year]["ATXPY"]
    ANNUITY_PENSION = Stage_II_targets[year]['Taxable Pensions and Annuities']*APOPN/ATXPY*1000 - annuity_pension.sum()

    ASCHEI = Stage_I_factors[year]["ASCHEI"]
    SCH_E_INCOME = Stage_II_targets[year]["Supplemental Income (Schedule E)"]*APOPN/ASCHEI*1000 - sch_e_income.sum()

    ASCHEL = Stage_I_factors[year]["ASCHEL"]
    SCH_E_LOSS = Stage_II_targets[year]["Supplemental Loss (Schedule E)"]*APOPN/ASCHEL*1000 - sch_e_loss.sum()

    ASOCSEC = Stage_I_factors[year]["ASOCSEC"]
    APOPSNR = Stage_I_factors[year]["APOPSNR"]
    SS_INCOME = Stage_II_targets[year]["Gross Social Security Income"]*APOPSNR/ASOCSEC*1000 - ss_income.sum()

    AUCOMP = Stage_I_factors[year]["AUCOMP"]
    UNEMPLOYMENT_COMP = Stage_II_targets[year]["Unemployment Compensation"]*APOPN/AUCOMP*1000 - unemployment_comp.sum()

    AWAGE = Stage_I_factors[year]["AWAGE"]
    WAGE_1 = Stage_II_targets[year]["Wages and Salaries: Zero or Less"]*APOPN/AWAGE*1000 - wage_1.sum()
    WAGE_2 = Stage_II_targets[year]["Wages and Salaries: $1 Less Than $10,000"]*APOPN/AWAGE*1000 - wage_2.sum()
    WAGE_3 = Stage_II_targets[year]["Wages and Salaries: $10,000 Less Than $20,000"]*APOPN/AWAGE*1000 - wage_3.sum()
    WAGE_4 = Stage_II_targets[year]["Wages and Salaries: $20,000 Less Than $30,000"]*APOPN/AWAGE*1000 - wage_4.sum()
    WAGE_5 = Stage_II_targets[year]["Wages and Salaries: $30,000 Less Than $40,000"]*APOPN/AWAGE*1000 - wage_5.sum()
    WAGE_6 = Stage_II_targets[year]["Wages and Salaries: $40,000 Less Than $50,000"]*APOPN/AWAGE*1000 - wage_6.sum()
    WAGE_7 = Stage_II_targets[year]["Wages and Salaries: $50,000 Less Than $75,000"]*APOPN/AWAGE*1000 - wage_7.sum()
    WAGE_8 = Stage_II_targets[year]["Wages and Salaries: $75,000 Less Than $100,000"]*APOPN/AWAGE*1000 - wage_8.sum()
    WAGE_9 = Stage_II_targets[year]["Wages and Salaries: $100,000 Less Than $200,000"]*APOPN/AWAGE*1000 - wage_9.sum()
    WAGE_10 = Stage_II_targets[year]["Wages and Salaries: $200,000 Less Than $500,000"]*APOPN/AWAGE*1000 - wage_10.sum()
    WAGE_11 = Stage_II_targets[year]["Wages and Salaries: $500,000 Less Than $1 Million"]*APOPN/AWAGE*1000 - wage_11.sum()
    WAGE_12 = Stage_II_targets[year]["Wages and Salaries: $1 Million and Over"]*APOPN/AWAGE*1000 - wage_12.sum()



    temp = [INTEREST,DIVIDEND, BIZ_INCOME, BIZ_LOSS, CAP_GAIN, ANNUITY_PENSION, SCH_E_INCOME, SCH_E_LOSS, SS_INCOME, UNEMPLOYMENT_COMP,
            WAGE_1,WAGE_2, WAGE_3,WAGE_4, WAGE_5, WAGE_6, WAGE_7,WAGE_8,WAGE_9, WAGE_10, WAGE_11, WAGE_12]
    for m in temp:
        b.append(m)

    targets = CyLPArray(b)
    print("Targets for year ", year, " is ", targets)

    LP = CyLPModel()

    r = LP.addVariable('r', length)
    s = LP.addVariable('s', length)

    print("Adding constraints")
    LP.addConstraint(r >=0, "positive r")
    LP.addConstraint(s >=0, "positive s")
    LP.addConstraint(r + s <= tol, "abs upperbound")

    c = CyLPArray((np.ones(length)))
    LP.objective = c * r + c * s




    LP.addConstraint(A1 * r + A2 * s == targets, "Aggregates")

    print("Setting up the LP model")
    model = CyClpSimplex(LP)


    print("Solving LP......")
    model.initialSolve()

    print("DONE!!")
    z = np.empty([length])
    z = (1+model.primalVariableSolution['r'] - model.primalVariableSolution['s'])*s006 * 100
    return z