//------------------------------------------------------------------------------
// LDL/Source/ldl.c: sparse LDL' factorization
//------------------------------------------------------------------------------

// LDL, Copyright (c) 2005-2022 by Timothy A. Davis. All Rights Reserved.
// SPDX-License-Identifier: LGPL-2.1+

//------------------------------------------------------------------------------

/* LDL:  a simple set of routines for sparse LDL' factorization.  These routines
 * are not terrifically fast (they do not use dense matrix kernels), but the
 * code is very short.  The purpose is to illustrate the algorithms in a very
 * concise manner, primarily for educational purposes.  Although the code is
 * very concise, this package is slightly faster than the built-in sparse
 * Cholesky factorization in MATLAB 7.0 (chol), when using the same input
 * permutation.
 *
 * The routines compute the LDL' factorization of a real sparse symmetric
 * matrix A (or PAP' if a permutation P is supplied), and solve upper
 * and lower triangular systems with the resulting L and D factors.  If A is
 * positive definite then the factorization will be accurate.  A can be
 * indefinite (with negative values on the diagonal D), but in this case no
 * guarantee of accuracy is provided, since no numeric pivoting is performed.
 *
 * The n-by-n sparse matrix A is in compressed-column form.  The nonzero values
 * in column j are stored in Ax [Ap [j] ... Ap [j+1]-1], with corresponding row
 * indices in Ai [Ap [j] ... Ap [j+1]-1].  Ap [0] = 0 is required, and thus
 * nz = Ap [n] is the number of nonzeros in A.  Ap is an int array of size n+1.
 * The int array Ai and the double array Ax are of size nz.  This data structure
 * is identical to the one used by MATLAB, except for the following
 * generalizations.  The row indices in each column of A need not be in any
 * particular order, although they must be in the range 0 to n-1.  Duplicate
 * entries can be present; any duplicates are summed.  That is, if row index i
 * appears twice in a column j, then the value of A (i,j) is the sum of the two
 * entries.  The data structure used here for the input matrix A is more
 * flexible than MATLAB's, which requires sorted columns with no duplicate
 * entries.
 *
 * Only the diagonal and upper triangular part of A (or PAP' if a permutation
 * P is provided) is accessed.  The lower triangular parts of the matrix A or
 * PAP' can be present, but they are ignored.
 *
 * The optional input permutation is provided as an array P of length n.  If
 * P [k] = j, the row and column j of A is the kth row and column of PAP'.
 * If P is present then the factorization is LDL' = PAP' or L*D*L' = A(P,P) in
 * 0-based MATLAB notation.  If P is not present (a null pointer) then no
 * permutation is performed, and the factorization is LDL' = A.
 *
 * The lower triangular matrix L is stored in the same compressed-column
 * form (an int Lp array of size n+1, an int Li array of size Lp [n], and a
 * double array Lx of the same size as Li).  It has a unit diagonal, which is
 * not stored.  The row indices in each column of L are always returned in
 * ascending order, with no duplicate entries.  This format is compatible with
 * MATLAB, except that it would be more convenient for MATLAB to include the
 * unit diagonal of L.  Doing so here would add additional complexity to the
 * code, and is thus omitted in the interest of keeping this code short and
 * readable.
 *
 * The elimination tree is held in the Parent [0..n-1] array.  It is normally
 * not required by the user, but it is required by ldl_numeric.  The diagonal
 * matrix D is held as an array D [0..n-1] of size n.
 *
 * --------------------
 * C-callable routines:
 * --------------------
 *
 *    ldl_symbolic:  Given the pattern of A, computes the Lp and Parent arrays
 *        required by ldl_numeric.  Takes time proportional to the number of
 *        nonzeros in L.  Computes the inverse Pinv of P if P is provided.
 *        Also returns Lnz, the count of nonzeros in each column of L below
 *        the diagonal (this is not required by ldl_numeric).
 *    ldl_numeric:  Given the pattern and numerical values of A, the Lp array,
 *        the Parent array, and P and Pinv if applicable, computes the
 *        pattern and numerical values of L and D.
 *    ldl_lsolve:  Solves Lx=b for a dense vector b.
 *    ldl_dsolve:  Solves Dx=b for a dense vector b.
 *    ldl_ltsolve: Solves L'x=b for a dense vector b.
 *    ldl_perm:    Computes x=Pb for a dense vector b.
 *    ldl_permt:   Computes x=P'b for a dense vector b.
 *    ldl_valid_perm:  checks the validity of a permutation vector
 *    ldl_valid_matrix:  checks the validity of the sparse matrix A
 *
 * ----------------------------
 * Limitations of this package:
 * ----------------------------
 *
 * In the interest of keeping this code simple and readable, ldl_symbolic and
 * ldl_numeric assume their inputs are valid.  You can check your own inputs
 * prior to calling these routines with the ldl_valid_perm and ldl_valid_matrix
 * routines.  Except for the two ldl_valid_* routines, no routine checks to see
 * if the array arguments are present (non-NULL).  Like all C routines, no
 * routine can determine if the arrays are long enough and don't overlap.
 *
 * The ldl_numeric does check the numerical factorization, however.  It returns
 * n if the factorization is successful.  If D (k,k) is zero, then k is
 * returned, and L is only partially computed.
 *
 * No pivoting to control fill-in is performed, which is often critical for
 * obtaining good performance.  I recommend that you compute the permutation P
 * using AMD or SYMAMD (approximate minimum degree ordering routines), or an
 * appropriate graph-partitioning based ordering.  See the ldldemo.m routine for
 * an example in MATLAB, and the ldlmain.c stand-alone C program for examples of
 * how to find P.  Routines for manipulating compressed-column matrices are
 * available in UMFPACK.  AMD, SYMAMD, UMFPACK, and this LDL package are all
 * available at http://www.suitesparse.com.
 *
 * -------------------------
 * Possible simplifications:
 * -------------------------
 *
 * These routines could be made even simpler with a few additional assumptions.
 * If no input permutation were performed, the caller would have to permute the
 * matrix first, but the computation of Pinv, and the use of P and Pinv could be
 * removed.  If only the diagonal and upper triangular part of A or PAP' are
 * present, then the tests in the "if (i < k)" statement in ldl_symbolic and
 * "if (i <= k)" in ldl_numeric, are always true, and could be removed (i can
 * equal k in ldl_symbolic, but then the body of the if statement would
 * correctly do no work since Flag [k] == k).  If we could assume that no
 * duplicate entries are present, then the statement Y [i] += Ax [p] could be
 * replaced with Y [i] = Ax [p] in ldl_numeric.
 *
 * --------------------------
 * Description of the method:
 * --------------------------
 *
 * LDL computes the symbolic factorization by finding the pattern of L one row
 * at a time.  It does this based on the following theory.  Consider a sparse
 * system Lx=b, where L, x, and b, are all sparse, and where L comes from a
 * Cholesky (or LDL') factorization.  The elimination tree (etree) of L is
 * defined as follows.  The parent of node j is the smallest k > j such that
 * L (k,j) is nonzero.  Node j has no parent if column j of L is completely zero
 * below the diagonal (j is a root of the etree in this case).  The nonzero
 * pattern of x is the union of the paths from each node i to the root, for
 * each nonzero b (i).  To compute the numerical solution to Lx=b, we can
 * traverse the columns of L corresponding to nonzero values of x.  This
 * traversal does not need to be done in the order 0 to n-1.  It can be done in
 * any "topological" order, such that x (i) is computed before x (j) if i is a
 * descendant of j in the elimination tree.
 *
 * The row-form of the LDL' factorization is shown in the MATLAB function
 * ldlrow.m in this LDL package.  Note that row k of L is found via a sparse
 * triangular solve of L (1:k-1, 1:k-1) \ A (1:k-1, k), to use 1-based MATLAB
 * notation.  Thus, we can start with the nonzero pattern of the kth column of
 * A (above the diagonal), follow the paths up to the root of the etree of the
 * (k-1)-by-(k-1) leading submatrix of L, and obtain the pattern of the kth row
 * of L.  Note that we only need the leading (k-1)-by-(k-1) submatrix of L to
 * do this.  The elimination tree can be constructed as we go.
 *
 * The symbolic factorization does the same thing, except that it discards the
 * pattern of L as it is computed.  It simply counts the number of nonzeros in
 * each column of L and then constructs the Lp index array when it's done.  The
 * symbolic factorization does not need to do this in topological order.
 * Compare ldl_symbolic with the first part of ldl_numeric, and note that the
 * while (len > 0) loop is not present in ldl_symbolic.
 *
 * Developed while on sabbatical at Stanford University and Lawrence Berkeley
 * National Laboratory.  Refer to the README file for the License.
 */

#include "hdsdp_cs.h"

/* ========================================================================== */
/* === ldl_symbolic ========================================================= */
/* ========================================================================== */

/* The input to this routine is a sparse matrix A, stored in column form, and
 * an optional permutation P.  The output is the elimination tree
 * and the number of nonzeros in each column of L.  Parent [i] = k if k is the
 * parent of i in the tree.  The Parent array is required by ldl_numeric.
 * Lnz [k] gives the number of nonzeros in the kth column of L, excluding the
 * diagonal.
 *
 * One workspace vector (Flag) of size n is required.
 *
 * If P is NULL, then it is ignored.  The factorization will be LDL' = A.
 * Pinv is not computed.  In this case, neither P nor Pinv are required by
 * ldl_numeric.
 *
 * If P is not NULL, then it is assumed to be a valid permutation.  If
 * row and column j of A is the kth pivot, the P [k] = j.  The factorization
 * will be LDL' = PAP', or A (p,p) in MATLAB notation.  The inverse permutation
 * Pinv is computed, where Pinv [j] = k if P [k] = j.  In this case, both P
 * and Pinv are required as inputs to ldl_numeric.
 *
 * The floating-point operation count of the subsequent call to ldl_numeric
 * is not returned, but could be computed after ldl_symbolic is done.  It is
 * the sum of (Lnz [k]) * (Lnz [k] + 2) for k = 0 to n-1.
 */

void LDL_symbolic
(
    int n,        /* A and L are n-by-n, where n >= 0 */
    int Ap [ ],    /* input of size n+1, not modified */
    int Ai [ ],    /* input of size nz=Ap[n], not modified */
    int Lp [ ],    /* output of size n+1, not defined on input */
    int Parent [ ],    /* output of size n, not defined on input */
    int Lnz [ ],    /* output of size n, not defined on input */
    int Flag [ ],    /* workspace of size n, not defn. on input or output */
    int P [ ],    /* optional input of size n */
    int Pinv [ ]    /* optional output of size n (used if P is not NULL) */
) {
    int i, k, p, kk, p2 ;
    if (P)
    {
    /* If P is present then compute Pinv, the inverse of P */
    for (k = 0 ; k < n ; k++)
    {
        Pinv [P [k]] = k ;
    }
    }
    for (k = 0 ; k < n ; k++)
    {
    /* L(k,:) pattern: all nodes reachable in etree from nz in A(0:k-1,k) */
    Parent [k] = -1 ;        /* parent of k is not yet known */
    Flag [k] = k ;            /* mark node k as visited */
    Lnz [k] = 0 ;            /* count of nonzeros in column k of L */
    kk = (P) ? (P [k]) : (k) ;  /* kth original, or permuted, column */
    p2 = Ap [kk+1] ;
    for (p = Ap [kk] ; p < p2 ; p++)
    {
        /* A (i,k) is nonzero (original or permuted A) */
        i = (Pinv) ? (Pinv [Ai [p]]) : (Ai [p]) ;
        if (i < k)
        {
        /* follow path from i to root of etree, stop at flagged node */
        for ( ; Flag [i] != k ; i = Parent [i])
        {
            /* find parent of i if not yet determined */
            if (Parent [i] == -1) Parent [i] = k ;
            Lnz [i]++ ;                /* L (k,i) is nonzero */
            Flag [i] = k ;            /* mark i as visited */
        }
        }
    }
    }
    /* construct Lp index array from Lnz column counts */
    Lp [0] = 0 ;
    for (k = 0 ; k < n ; k++)
    {
    Lp [k+1] = Lp [k] + Lnz [k] ;
    }
}


/* ========================================================================== */
/* === ldl_numeric ========================================================== */
/* ========================================================================== */

/* Given a sparse matrix A (the arguments n, Ap, Ai, and Ax) and its symbolic
 * analysis (Lp and Parent, and optionally P and Pinv), compute the numeric LDL'
 * factorization of A or PAP'.  The outputs of this routine are arguments Li,
 * Lx, and D.  It also requires three size-n workspaces (Y, Pattern, and Flag).
 */

int LDL_numeric    /* returns n if successful, k if D (k,k) is zero */
(
    int n,        /* A and L are n-by-n, where n >= 0 */
    int Ap [ ],    /* input of size n+1, not modified */
    int Ai [ ],    /* input of size nz=Ap[n], not modified */
    double Ax [ ],    /* input of size nz=Ap[n], not modified */
    int Lp [ ],    /* input of size n+1, not modified */
    int Parent [ ],    /* input of size n, not modified */
    int Lnz [ ],    /* output of size n, not defn. on input */
    int Li [ ],    /* output of size lnz=Lp[n], not defined on input */
    double Lx [ ],    /* output of size lnz=Lp[n], not defined on input */
    double D [ ],    /* output of size n, not defined on input */
    double Y [ ],    /* workspace of size n, not defn. on input or output */
    int Pattern [ ],/* workspace of size n, not defn. on input or output */
    int Flag [ ],    /* workspace of size n, not defn. on input or output */
    int P [ ],    /* optional input of size n */
    int Pinv [ ]    /* optional input of size n */
) {
    double yi, l_ki ;
    int i, k, p, kk, p2, len, top ;
    for (k = 0 ; k < n ; k++)
    {
    /* compute nonzero Pattern of kth row of L, in topological order */
    Y [k] = 0.0 ;            /* Y(0:k) is now all zero */
    top = n ;            /* stack for pattern is empty */
    Flag [k] = k ;            /* mark node k as visited */
    Lnz [k] = 0 ;            /* count of nonzeros in column k of L */
    kk = (P) ? (P [k]) : (k) ;  /* kth original, or permuted, column */
    p2 = Ap [kk+1] ;
    for (p = Ap [kk] ; p < p2 ; p++)
    {
        i = (Pinv) ? (Pinv [Ai [p]]) : (Ai [p]) ;    /* get A(i,k) */
        if (i <= k)
        {
        Y [i] += Ax [p] ;  /* scatter A(i,k) into Y (sum duplicates) */
        for (len = 0 ; Flag [i] != k ; i = Parent [i])
        {
            Pattern [len++] = i ;   /* L(k,i) is nonzero */
            Flag [i] = k ;        /* mark i as visited */
        }
        while (len > 0) Pattern [--top] = Pattern [--len] ;
        }
    }
    /* compute numerical values kth row of L (a sparse triangular solve) */
    D [k] = Y [k] ;            /* get D(k,k) and clear Y(k) */
    Y [k] = 0.0 ;
    for ( ; top < n ; top++)
    {
        i = Pattern [top] ;        /* Pattern [top:n-1] is pattern of L(k,:) */
        yi = Y [i] ;        /* get and clear Y(i) */
        Y [i] = 0.0 ;
        p2 = Lp [i] + Lnz [i] ;
        for (p = Lp [i] ; p < p2 ; p++)
        {
        Y [Li [p]] -= Lx [p] * yi ;
        }
        l_ki = yi / D [i] ;        /* the nonzero entry L(k,i) */
        D [k] -= l_ki * yi ;
        Li [p] = k ;        /* store L(k,i) in column form of L */
        Lx [p] = l_ki ;
        Lnz [i]++ ;            /* increment count of nonzeros in col i */
    }
    if (D [k] == 0.0) return (k) ;        /* failure, D(k,k) is zero */
    }
    return (n) ;    /* success, diagonal of D is all nonzero */
}


/* ========================================================================== */
/* === ldl_lsolve:  solve Lx=b ============================================== */
/* ========================================================================== */

void LDL_lsolve
(
    int n,        /* L is n-by-n, where n >= 0 */
    double X [ ],    /* size n.  right-hand-side on input, soln. on output */
    int Lp [ ],    /* input of size n+1, not modified */
    int Li [ ],    /* input of size lnz=Lp[n], not modified */
    double Lx [ ]    /* input of size lnz=Lp[n], not modified */
) {
    int j, p, p2 ;
    for (j = 0 ; j < n ; j++)
    {
    p2 = Lp [j+1] ;
    for (p = Lp [j] ; p < p2 ; p++)
    {
        X [Li [p]] -= Lx [p] * X [j] ;
    }
    }
}


/* ========================================================================== */
/* === ldl_dsolve:  solve Dx=b ============================================== */
/* ========================================================================== */

void LDL_dsolve
(
    int n,        /* D is n-by-n, where n >= 0 */
    double X [ ],    /* size n.  right-hand-side on input, soln. on output */
    double D [ ]    /* input of size n, not modified */
) {
    int j ;
    for (j = 0 ; j < n ; j++)
    {
    X [j] /= D [j] ;
    }
}


/* ========================================================================== */
/* === ldl_ltsolve: solve L'x=b  ============================================ */
/* ========================================================================== */

void LDL_ltsolve
(
    int n,        /* L is n-by-n, where n >= 0 */
    double X [ ],    /* size n.  right-hand-side on input, soln. on output */
    int Lp [ ],    /* input of size n+1, not modified */
    int Li [ ],    /* input of size lnz=Lp[n], not modified */
    double Lx [ ]    /* input of size lnz=Lp[n], not modified */
) {
    int j, p, p2 ;
    for (j = n-1 ; j >= 0 ; j--)
    {
    p2 = Lp [j+1] ;
    for (p = Lp [j] ; p < p2 ; p++)
    {
        X [j] -= Lx [p] * X [Li [p]] ;
    }
    }
}
