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450 lines
16 KiB
450 lines
16 KiB
"""
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Routines for removing redundant (linearly dependent) equations from linear
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programming equality constraints.
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"""
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# Author: Matt Haberland
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import numpy as np
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from scipy.linalg import svd
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import scipy
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from scipy.linalg.blas import dtrsm
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def _row_count(A):
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"""
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Counts the number of nonzeros in each row of input array A.
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Nonzeros are defined as any element with absolute value greater than
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tol = 1e-13. This value should probably be an input to the function.
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Parameters
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----------
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A : 2-D array
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An array representing a matrix
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Returns
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-------
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rowcount : 1-D array
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Number of nonzeros in each row of A
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"""
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tol = 1e-13
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return np.array((abs(A) > tol).sum(axis=1)).flatten()
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def _get_densest(A, eligibleRows):
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"""
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Returns the index of the densest row of A. Ignores rows that are not
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eligible for consideration.
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Parameters
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----------
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A : 2-D array
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An array representing a matrix
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eligibleRows : 1-D logical array
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Values indicate whether the corresponding row of A is eligible
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to be considered
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Returns
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-------
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i_densest : int
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Index of the densest row in A eligible for consideration
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"""
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rowCounts = _row_count(A)
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return np.argmax(rowCounts * eligibleRows)
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def _remove_zero_rows(A, b):
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"""
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Eliminates trivial equations from system of equations defined by Ax = b
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and identifies trivial infeasibilities
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Parameters
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----------
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A : 2-D array
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An array representing the left-hand side of a system of equations
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b : 1-D array
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An array representing the right-hand side of a system of equations
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Returns
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-------
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A : 2-D array
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An array representing the left-hand side of a system of equations
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b : 1-D array
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An array representing the right-hand side of a system of equations
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status: int
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An integer indicating the status of the removal operation
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0: No infeasibility identified
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2: Trivially infeasible
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message : str
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A string descriptor of the exit status of the optimization.
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"""
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status = 0
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message = ""
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i_zero = _row_count(A) == 0
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A = A[np.logical_not(i_zero), :]
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if not(np.allclose(b[i_zero], 0)):
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status = 2
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message = "There is a zero row in A_eq with a nonzero corresponding " \
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"entry in b_eq. The problem is infeasible."
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b = b[np.logical_not(i_zero)]
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return A, b, status, message
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def bg_update_dense(plu, perm_r, v, j):
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LU, p = plu
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vperm = v[perm_r]
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u = dtrsm(1, LU, vperm, lower=1, diag=1)
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LU[:j+1, j] = u[:j+1]
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l = u[j+1:]
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piv = LU[j, j]
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LU[j+1:, j] += (l/piv)
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return LU, p
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def _remove_redundancy_dense(A, rhs, true_rank=None):
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"""
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Eliminates redundant equations from system of equations defined by Ax = b
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and identifies infeasibilities.
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Parameters
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----------
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A : 2-D sparse matrix
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An matrix representing the left-hand side of a system of equations
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rhs : 1-D array
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An array representing the right-hand side of a system of equations
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Returns
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----------
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A : 2-D sparse matrix
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A matrix representing the left-hand side of a system of equations
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rhs : 1-D array
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An array representing the right-hand side of a system of equations
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status: int
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An integer indicating the status of the system
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0: No infeasibility identified
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2: Trivially infeasible
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message : str
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A string descriptor of the exit status of the optimization.
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References
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----------
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.. [2] Andersen, Erling D. "Finding all linearly dependent rows in
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large-scale linear programming." Optimization Methods and Software
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6.3 (1995): 219-227.
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"""
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tolapiv = 1e-8
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tolprimal = 1e-8
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status = 0
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message = ""
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inconsistent = ("There is a linear combination of rows of A_eq that "
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"results in zero, suggesting a redundant constraint. "
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"However the same linear combination of b_eq is "
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"nonzero, suggesting that the constraints conflict "
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"and the problem is infeasible.")
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A, rhs, status, message = _remove_zero_rows(A, rhs)
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if status != 0:
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return A, rhs, status, message
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m, n = A.shape
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v = list(range(m)) # Artificial column indices.
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b = list(v) # Basis column indices.
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# This is better as a list than a set because column order of basis matrix
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# needs to be consistent.
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d = [] # Indices of dependent rows
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perm_r = None
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A_orig = A
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A = np.zeros((m, m + n), order='F')
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np.fill_diagonal(A, 1)
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A[:, m:] = A_orig
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e = np.zeros(m)
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js_candidates = np.arange(m, m+n, dtype=int) # candidate columns for basis
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# manual masking was faster than masked array
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js_mask = np.ones(js_candidates.shape, dtype=bool)
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# Implements basic algorithm from [2]
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# Uses some of the suggested improvements (removing zero rows and
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# Bartels-Golub update idea).
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# Removing column singletons would be easy, but it is not as important
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# because the procedure is performed only on the equality constraint
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# matrix from the original problem - not on the canonical form matrix,
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# which would have many more column singletons due to slack variables
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# from the inequality constraints.
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# The thoughts on "crashing" the initial basis are only really useful if
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# the matrix is sparse.
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lu = np.eye(m, order='F'), np.arange(m) # initial LU is trivial
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perm_r = lu[1]
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for i in v:
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e[i] = 1
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if i > 0:
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e[i-1] = 0
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try: # fails for i==0 and any time it gets ill-conditioned
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j = b[i-1]
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lu = bg_update_dense(lu, perm_r, A[:, j], i-1)
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except Exception:
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lu = scipy.linalg.lu_factor(A[:, b])
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LU, p = lu
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perm_r = list(range(m))
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for i1, i2 in enumerate(p):
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perm_r[i1], perm_r[i2] = perm_r[i2], perm_r[i1]
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pi = scipy.linalg.lu_solve(lu, e, trans=1)
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js = js_candidates[js_mask]
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batch = 50
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# This is a tiny bit faster than looping over columns indivually,
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# like for j in js: if abs(A[:,j].transpose().dot(pi)) > tolapiv:
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for j_index in range(0, len(js), batch):
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j_indices = js[j_index: min(j_index+batch, len(js))]
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c = abs(A[:, j_indices].transpose().dot(pi))
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if (c > tolapiv).any():
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j = js[j_index + np.argmax(c)] # very independent column
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b[i] = j
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js_mask[j-m] = False
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break
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else:
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bibar = pi.T.dot(rhs.reshape(-1, 1))
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bnorm = np.linalg.norm(rhs)
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if abs(bibar)/(1+bnorm) > tolprimal: # inconsistent
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status = 2
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message = inconsistent
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return A_orig, rhs, status, message
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else: # dependent
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d.append(i)
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if true_rank is not None and len(d) == m - true_rank:
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break # found all redundancies
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keep = set(range(m))
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keep = list(keep - set(d))
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return A_orig[keep, :], rhs[keep], status, message
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def _remove_redundancy_sparse(A, rhs):
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"""
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Eliminates redundant equations from system of equations defined by Ax = b
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and identifies infeasibilities.
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Parameters
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----------
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A : 2-D sparse matrix
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An matrix representing the left-hand side of a system of equations
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rhs : 1-D array
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An array representing the right-hand side of a system of equations
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Returns
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-------
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A : 2-D sparse matrix
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A matrix representing the left-hand side of a system of equations
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rhs : 1-D array
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An array representing the right-hand side of a system of equations
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status: int
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An integer indicating the status of the system
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0: No infeasibility identified
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2: Trivially infeasible
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message : str
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A string descriptor of the exit status of the optimization.
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References
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----------
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.. [2] Andersen, Erling D. "Finding all linearly dependent rows in
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large-scale linear programming." Optimization Methods and Software
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6.3 (1995): 219-227.
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"""
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tolapiv = 1e-8
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tolprimal = 1e-8
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status = 0
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message = ""
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inconsistent = ("There is a linear combination of rows of A_eq that "
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"results in zero, suggesting a redundant constraint. "
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"However the same linear combination of b_eq is "
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"nonzero, suggesting that the constraints conflict "
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"and the problem is infeasible.")
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A, rhs, status, message = _remove_zero_rows(A, rhs)
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if status != 0:
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return A, rhs, status, message
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m, n = A.shape
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v = list(range(m)) # Artificial column indices.
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b = list(v) # Basis column indices.
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# This is better as a list than a set because column order of basis matrix
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# needs to be consistent.
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k = set(range(m, m+n)) # Structural column indices.
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d = [] # Indices of dependent rows
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A_orig = A
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A = scipy.sparse.hstack((scipy.sparse.eye(m), A)).tocsc()
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e = np.zeros(m)
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# Implements basic algorithm from [2]
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# Uses only one of the suggested improvements (removing zero rows).
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# Removing column singletons would be easy, but it is not as important
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# because the procedure is performed only on the equality constraint
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# matrix from the original problem - not on the canonical form matrix,
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# which would have many more column singletons due to slack variables
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# from the inequality constraints.
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# The thoughts on "crashing" the initial basis sound useful, but the
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# description of the procedure seems to assume a lot of familiarity with
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# the subject; it is not very explicit. I already went through enough
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# trouble getting the basic algorithm working, so I was not interested in
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# trying to decipher this, too. (Overall, the paper is fraught with
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# mistakes and ambiguities - which is strange, because the rest of
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# Andersen's papers are quite good.)
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# I tried and tried and tried to improve performance using the
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# Bartels-Golub update. It works, but it's only practical if the LU
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# factorization can be specialized as described, and that is not possible
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# until the SciPy SuperLU interface permits control over column
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# permutation - see issue #7700.
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for i in v:
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B = A[:, b]
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e[i] = 1
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if i > 0:
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e[i-1] = 0
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pi = scipy.sparse.linalg.spsolve(B.transpose(), e).reshape(-1, 1)
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js = list(k-set(b)) # not efficient, but this is not the time sink...
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# Due to overhead, it tends to be faster (for problems tested) to
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# compute the full matrix-vector product rather than individual
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# vector-vector products (with the chance of terminating as soon
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# as any are nonzero). For very large matrices, it might be worth
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# it to compute, say, 100 or 1000 at a time and stop when a nonzero
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# is found.
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c = (np.abs(A[:, js].transpose().dot(pi)) > tolapiv).nonzero()[0]
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if len(c) > 0: # independent
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j = js[c[0]]
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# in a previous commit, the previous line was changed to choose
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# index j corresponding with the maximum dot product.
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# While this avoided issues with almost
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# singular matrices, it slowed the routine in most NETLIB tests.
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# I think this is because these columns were denser than the
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# first column with nonzero dot product (c[0]).
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# It would be nice to have a heuristic that balances sparsity with
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# high dot product, but I don't think it's worth the time to
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# develop one right now. Bartels-Golub update is a much higher
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# priority.
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b[i] = j # replace artificial column
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else:
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bibar = pi.T.dot(rhs.reshape(-1, 1))
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bnorm = np.linalg.norm(rhs)
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if abs(bibar)/(1 + bnorm) > tolprimal:
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status = 2
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message = inconsistent
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return A_orig, rhs, status, message
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else: # dependent
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d.append(i)
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keep = set(range(m))
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keep = list(keep - set(d))
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return A_orig[keep, :], rhs[keep], status, message
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def _remove_redundancy(A, b):
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"""
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Eliminates redundant equations from system of equations defined by Ax = b
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and identifies infeasibilities.
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Parameters
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----------
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A : 2-D array
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An array representing the left-hand side of a system of equations
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b : 1-D array
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An array representing the right-hand side of a system of equations
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Returns
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-------
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A : 2-D array
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An array representing the left-hand side of a system of equations
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b : 1-D array
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An array representing the right-hand side of a system of equations
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status: int
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An integer indicating the status of the system
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0: No infeasibility identified
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2: Trivially infeasible
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message : str
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A string descriptor of the exit status of the optimization.
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References
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----------
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.. [2] Andersen, Erling D. "Finding all linearly dependent rows in
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large-scale linear programming." Optimization Methods and Software
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6.3 (1995): 219-227.
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"""
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A, b, status, message = _remove_zero_rows(A, b)
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if status != 0:
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return A, b, status, message
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U, s, Vh = svd(A)
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eps = np.finfo(float).eps
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tol = s.max() * max(A.shape) * eps
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m, n = A.shape
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s_min = s[-1] if m <= n else 0
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# this algorithm is faster than that of [2] when the nullspace is small
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# but it could probably be improvement by randomized algorithms and with
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# a sparse implementation.
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# it relies on repeated singular value decomposition to find linearly
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# dependent rows (as identified by columns of U that correspond with zero
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# singular values). Unfortunately, only one row can be removed per
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# decomposition (I tried otherwise; doing so can cause problems.)
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# It would be nice if we could do truncated SVD like sp.sparse.linalg.svds
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# but that function is unreliable at finding singular values near zero.
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# Finding max eigenvalue L of A A^T, then largest eigenvalue (and
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# associated eigenvector) of -A A^T + L I (I is identity) via power
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# iteration would also work in theory, but is only efficient if the
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# smallest nonzero eigenvalue of A A^T is close to the largest nonzero
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# eigenvalue.
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while abs(s_min) < tol:
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v = U[:, -1] # TODO: return these so user can eliminate from problem?
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# rows need to be represented in significant amount
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eligibleRows = np.abs(v) > tol * 10e6
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if not np.any(eligibleRows) or np.any(np.abs(v.dot(A)) > tol):
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status = 4
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message = ("Due to numerical issues, redundant equality "
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"constraints could not be removed automatically. "
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"Try providing your constraint matrices as sparse "
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"matrices to activate sparse presolve, try turning "
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"off redundancy removal, or try turning off presolve "
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"altogether.")
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break
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if np.any(np.abs(v.dot(b)) > tol * 100): # factor of 100 to fix 10038 and 10349
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status = 2
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message = ("There is a linear combination of rows of A_eq that "
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"results in zero, suggesting a redundant constraint. "
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"However the same linear combination of b_eq is "
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"nonzero, suggesting that the constraints conflict "
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"and the problem is infeasible.")
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break
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i_remove = _get_densest(A, eligibleRows)
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A = np.delete(A, i_remove, axis=0)
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b = np.delete(b, i_remove)
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U, s, Vh = svd(A)
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m, n = A.shape
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s_min = s[-1] if m <= n else 0
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return A, b, status, message
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