constrained_majorization.c

Go to the documentation of this file.

/*************************************************************************
 * Copyright (c) 2011 AT&T Intellectual Property 
 * All rights reserved. This program and the accompanying materials
 * are made available under the terms of the Eclipse Public License v1.0
 * which accompanies this distribution, and is available at
 * https://www.eclipse.org/legal/epl-v10.html
 *
 * Contributors: Details at https://graphviz.org
 *************************************************************************/
 
#include "config.h"
 
#include <neatogen/digcola.h>
#include <util/alloc.h>
#ifdef DIGCOLA
#include <math.h>
#include <stdbool.h>
#include <stdlib.h>
#include <time.h>
#include <stdio.h>
#include <float.h>
#include <neatogen/stress.h>
#include <neatogen/dijkstra.h>
#include <neatogen/bfs.h>
#include <neatogen/matrix_ops.h>
#include <neatogen/kkutils.h>
#include <neatogen/conjgrad.h>
#include <neatogen/quad_prog_solver.h>
#include <neatogen/matrix_ops.h>
 
#define localConstrMajorIterations 15
#define levels_sep_tol 1e-1
 
int stress_majorization_with_hierarchy(vtx_data * graph,        /* Input graph in sparse representation  */
                                       int n,   /* Number of nodes */
                                       double **d_coords,       /* Coordinates of nodes (output layout)  */
                                       node_t ** nodes, /* Original nodes */
                                       int dim, /* Dimensionality of layout */
                                       int opts,        /* options */
                                       int model,       /* difference model */
                                       int maxi,        /* max iterations */
                                       double levels_gap)
{
    int iterations = 0;         /* Output: number of iteration of the process */
 
        /*************************************************
        ** Computation of full, dense, unrestricted k-D ** 
        ** stress minimization by majorization          ** 
        ** This function imposes HIERARCHY CONSTRAINTS  **
        *************************************************/
 
    bool directionalityExist = false;
    float *lap1 = NULL;
    float *dist_accumulator = NULL;
    float *tmp_coords = NULL;
    float **b = NULL;
    double *degrees = NULL;
    float *lap2 = NULL;
    float *f_storage = NULL;
    float **coords = NULL;
 
    const double conj_tol = tolerance_cg; // tolerance of Conjugate Gradient
    CMajEnv *cMajEnv = NULL;
    const bool smart_ini = !!(opts & opt_smart_init);
    float *Dij = NULL;
    /* to compensate noises, we never consider gaps smaller than 'abs_tol' */
    const double abs_tol = 1e-2;
    /* Additionally, we never consider gaps smaller than 'abs_tol'*<avg_gap> */
    const double relative_tol = levels_sep_tol;
    int *ordering = NULL, *levels = NULL;
    bool converged;
    int num_levels;
 
    if (graph[0].edists != NULL) {
        for (int i = 0; i < n; i++) {
            for (size_t j = 1; j < graph[i].nedges; j++) {
                directionalityExist |= graph[i].edists[j] != 0;
            }
        }
    }
    if (!directionalityExist) {
        return stress_majorization_kD_mkernel(graph, n,
                                              d_coords, nodes, dim, opts,
                                              model, maxi);
    }
 
        /******************************************************************
        ** First, partition nodes into layers: These are our constraints **
        ******************************************************************/
 
    if (smart_ini) {
        if (dim > 2) {
            /* the dim==2 case is handled below                      */
            if (stress_majorization_kD_mkernel(graph, n,
                                           d_coords + 1, nodes, dim - 1,
                                           opts, model, 15) < 0)
                return -1;
            /* now copy the y-axis into the (dim-1)-axis */
            for (int i = 0; i < n; i++) {
                d_coords[dim - 1][i] = d_coords[1][i];
            }
        }
 
        double *const x = d_coords[0];
        double *const y = d_coords[1];
        if (compute_y_coords(graph, n, y, n)) {
            iterations = -1;
            goto finish;
        }
        if (compute_hierarchy(graph, n, abs_tol, relative_tol, y, &ordering,
                          &levels, &num_levels)) {
            iterations = -1;
            goto finish;
        }
        if (num_levels < 1) {
            /* no hierarchy found, use faster algorithm */
            free(levels);
            return stress_majorization_kD_mkernel(graph, n,
                                                  d_coords, nodes, dim,
                                                  opts, model, maxi);
        }
 
        if (levels_gap > 0) {
            /* ensure that levels are separated in the initial layout */
            double displacement = 0;
            for (int i = 0; i < num_levels; i++) {
                displacement +=
                    MAX(0.0,
                        levels_gap - (y[ordering[levels[i]]] +
                                      displacement -
                                      y[ordering[levels[i] - 1]]));
                const int stop = i < num_levels - 1 ? levels[i + 1] : n;
                for (int j = levels[i]; j < stop; j++) {
                    y[ordering[j]] += displacement;
                }
            }
        }
        if (dim == 2) {
            if (IMDS_given_dim(graph, n, y, x, Epsilon)) {
                iterations = -1;
                goto finish;
            }
        }
    } else {
        initLayout(n, dim, d_coords, nodes);
        if (compute_hierarchy(graph, n, abs_tol, relative_tol, NULL, &ordering,
                          &levels, &num_levels)) {
            iterations = -1;
            goto finish;
        }
    }
    if (n == 1) {
        free(levels);
        return 0;
    }
 
        /****************************************************
        ** Compute the all-pairs-shortest-distances matrix **
        ****************************************************/
 
    if (maxi == 0) {
        free(levels);
        return iterations;
    }
 
    if (Verbose)
        start_timer();
 
    if (model == MODEL_SUBSET) {
        /* weight graph to separate high-degree nodes */
        /* and perform slower Dijkstra-based computation */
        if (Verbose)
            fprintf(stderr, "Calculating subset model");
        Dij = compute_apsp_artificial_weights_packed(graph, n);
    } else if (model == MODEL_CIRCUIT) {
        Dij = circuitModel(graph, n);
        if (!Dij) {
            agwarningf(
                  "graph is disconnected. Hence, the circuit model\n");
            agerr(AGPREV,
                  "is undefined. Reverting to the shortest path model.\n");
        }
    } else if (model == MODEL_MDS) {
        if (Verbose)
            fprintf(stderr, "Calculating MDS model");
        Dij = mdsModel(graph, n);
    }
    if (!Dij) {
        if (Verbose)
            fprintf(stderr, "Calculating shortest paths");
        Dij = compute_apsp_packed(graph, n);
    }
    if (Verbose) {
        fprintf(stderr, ": %.2f sec\n", elapsed_sec());
        fprintf(stderr, "Setting initial positions");
        start_timer();
    }
 
    const int length = n + n * (n - 1) / 2;
 
    if (!smart_ini) {
        /* for numerical stability, scale down layout            */
        /* No Jiggling, might conflict with constraints                  */
        double max = 1;
        for (int i = 0; i < dim; i++) {
            for (int j = 0; j < n; j++) {
                max = fmax(max, fabs(d_coords[i][j]));
            }
        }
        for (int i = 0; i < dim; i++) {
            for (int j = 0; j < n; j++) {
                d_coords[i][j] *= 10 / max;
            }
        }
    }
 
    if (levels_gap > 0) {
        const double sum1 = n * (n - 1) / 2;
        double sum2 = 0;
        for (int count = 0, i = 0; i < n - 1; i++) {
            count++;            // skip self distance
            for (int j = i + 1; j < n; j++, count++) {
                sum2 += distance_kD(d_coords, dim, i, j) / Dij[count];
            }
        }
        const float scale_ratio = (float)(sum2 / sum1);
        for (int i = 0; i < length; i++) {
            Dij[i] *= scale_ratio;
        }
    }
 
        /**************************
        ** Layout initialization **
        **************************/
 
    for (int i = 0; i < dim; i++) {
        orthog1(n, d_coords[i]);
    }
 
    /* for the y-coords, don't center them, but translate them so y[0]=0 */
    const double y_0 = d_coords[1][0];
    for (int i = 0; i < n; i++) {
        d_coords[1][i] -= y_0;
    }
 
    coords = gv_calloc(dim, sizeof(float *));
    f_storage = gv_calloc(dim * n, sizeof(float));
    for (int i = 0; i < dim; i++) {
        coords[i] = f_storage + i * n;
        for (int j = 0; j < n; j++) {
            coords[i][j] = (float)d_coords[i][j];
        }
    }
 
    /* compute constant term in stress sum
     * which is \sum_{i<j} w_{ij}d_{ij}^2
     */
    const double constant_term = n * (n - 1) / 2;
 
    if (Verbose)
        fprintf(stderr, ": %.2f sec", elapsed_sec());
 
        /**************************
        ** Laplacian computation **
        **************************/
 
    lap2 = Dij;
    const int lap_length = n + n * (n - 1) / 2;
    square_vec(lap_length, lap2);
    /* compute off-diagonal entries */
    invert_vec(lap_length, lap2);
 
    /* compute diagonal entries */
    degrees = gv_calloc(n, sizeof(double));
    set_vector_val(n, 0, degrees);
    for (int i = 0, count = 0; i < n - 1; i++) {
        double degree = 0;
        count++;                // skip main diag entry
        for (int j = 1; j < n - i; j++, count++) {
            const float val = lap2[count];
            degree += val;
            degrees[i + j] -= val;
        }
        degrees[i] -= degree;
    }
    for (int step = n, count = 0, i = 0; i < n; i++, count += step, step--) {
        lap2[count] = (float) degrees[i];
    }
 
        /*************************
        ** Layout optimization  **
        *************************/
 
    b = gv_calloc(dim, sizeof(float *));
    b[0] = gv_calloc(dim * n, sizeof(float));
    for (int k = 1; k < dim; k++) {
        b[k] = b[0] + k * n;
    }
 
    tmp_coords = gv_calloc(n, sizeof(float));
    dist_accumulator = gv_calloc(n, sizeof(float));
    lap1 = gv_calloc(lap_length, sizeof(float));
 
    double old_stress = DBL_MAX; // at least one iteration
 
    cMajEnv =
        initConstrainedMajorization(lap2, n, ordering, levels, num_levels);
 
    for (converged = false, iterations = 0;
         iterations < maxi && !converged; iterations++) {
 
        /* First, construct Laplacian of 1/(d_ij*|p_i-p_j|)  */
        set_vector_val(n, 0, degrees);
        sqrt_vecf(lap_length, lap2, lap1);
        for (int count = 0, i = 0; i < n - 1; i++) {
            const int len = n - i - 1;
            /* init 'dist_accumulator' with zeros */
            set_vector_valf(n, 0, dist_accumulator);
 
            /* put into 'dist_accumulator' all squared distances 
             * between 'i' and 'i'+1,...,'n'-1
             */
            for (int k = 0; k < dim; k++) {
                set_vector_valf(len, coords[k][i], tmp_coords);
                vectors_mult_additionf(len, tmp_coords, -1, coords[k] + i + 1);
                square_vec(len, tmp_coords);
                vectors_additionf(len, tmp_coords, dist_accumulator, dist_accumulator);
            }
 
            /* convert to 1/d_{ij} */
            invert_sqrt_vec(len, dist_accumulator);
            /* detect overflows */
            for (int j = 0; j < len; j++) {
                if (dist_accumulator[j] >= FLT_MAX || dist_accumulator[j] < 0) {
                    dist_accumulator[j] = 0;
                }
            }
 
            count++;            /* save place for the main diagonal entry */
            double degree = 0;
            for (int j = 0; j < len; j++, count++) {
                const float val = lap1[count] *= dist_accumulator[j];
                degree += val;
                degrees[i + j + 1] -= val;
            }
            degrees[i] -= degree;
        }
        for (int step = n, count = 0, i = 0; i < n; i++, count += step, step--) {
            lap1[count] = (float) degrees[i];
        }
 
        /* Now compute b[] (L^(X(t))*X(t)) */
        for (int k = 0; k < dim; k++) {
            /* b[k] := lap1*coords[k] */
            right_mult_with_vector_ff(lap1, n, coords[k], b[k]);
        }
 
        /* compute new stress
         * remember that the Laplacians are negated, so we subtract 
         * instead of add and vice versa
         */
        double new_stress = 0;
        for (int k = 0; k < dim; k++) {
            new_stress += vectors_inner_productf(n, coords[k], b[k]);
        }
        new_stress *= 2;
        new_stress += constant_term;    // only after mult by 2              
        for (int k = 0; k < dim; k++) {
            right_mult_with_vector_ff(lap2, n, coords[k], tmp_coords);
            new_stress -= vectors_inner_productf(n, coords[k], tmp_coords);
        }
 
        /* check for convergence */
        converged =
            fabs(new_stress - old_stress) / fabs(old_stress + 1e-10) <
            Epsilon;
        converged |= iterations > 1 && new_stress > old_stress;
        /* in first iteration we allowed stress increase, which 
         * might result ny imposing constraints
         */
        old_stress = new_stress;
 
        for (int k = 0; k < dim; k++) {
            /* now we find the optimizer of trace(X'LX)+X'B by solving 'dim' 
             * system of equations, thereby obtaining the new coordinates.
             * If we use the constraints (given by the var's: 'ordering', 
             * 'levels' and 'num_levels'), we cannot optimize 
             * trace(X'LX)+X'B by simply solving equations, but we have
             * to use a quadratic programming solver
             * note: 'lap2' is a packed symmetric matrix, that is its 
             * upper-triangular part is arranged in a vector row-wise
             * also note: 'lap2' is really the negated laplacian (the 
             * laplacian is -'lap2')
             */
 
            if (k == 1) {
                /* use quad solver in the y-dimension */
                constrained_majorization_new_with_gaps(cMajEnv, b[k],
                                                       coords, k,
                                                       localConstrMajorIterations,
                                                       levels_gap);
 
            } else {
                /* use conjugate gradient for all dimensions except y */
                if (conjugate_gradient_mkernel(lap2, coords[k], b[k], n,
                                           conj_tol, n)) {
                    iterations = -1;
                    goto finish;
                }
            }
        }
    }
 
    for (int i = 0; i < dim; i++) {
        for (int j = 0; j < n; j++) {
            d_coords[i][j] = coords[i][j];
        }
    }
 
    free(tmp_coords);
    free(dist_accumulator);
    free(degrees);
    free(lap2);
 
    free(lap1);
 
finish:
    if (cMajEnv != NULL) {
        deleteCMajEnv(cMajEnv);
    }
    if (b) {
        free(b[0]);
        free(b);
    }
    free(f_storage);
    free(coords);
    free(ordering);
 
    free(levels);
 
    return iterations;
}
#endif                          /* DIGCOLA */