stress.c

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/*************************************************************************
 * 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 v2.0
 * which accompanies this distribution, and is available at
 * https://www.eclipse.org/org/documents/epl-2.0/EPL-2.0.html
 *
 * Contributors: Details at https://graphviz.org
 *************************************************************************/
 
#include "config.h"
 
#include <float.h>
#include <neatogen/neato.h>
#include <neatogen/dijkstra.h>
#include <neatogen/bfs.h>
#include <neatogen/pca.h>
#include <neatogen/matrix_ops.h>
#include <neatogen/conjgrad.h>
#include <neatogen/embed_graph.h>
#include <neatogen/kkutils.h>
#include <neatogen/stress.h>
#include <math.h>
#include <stdbool.h>
#include <stdlib.h>
#include <time.h>
#include <util/alloc.h>
 
// the terms in the stress energy are normalized by dᵢⱼ¯²
 
/* dimensionality of subspace; relevant 
 * when optimizing within subspace) 
 */
#define stress_pca_dim 50
 
 /* a structure used for storing sparse distance matrix */
typedef struct {
    size_t nedges;
    int *edges;
    DistType *edist;
    bool free_mem;
} dist_data;
 
static double compute_stressf(float **coords, float *lap, int dim, int n, int exp)
{
    /* compute the overall stress */
 
    int i, j, l, neighbor, count;
    double sum, dist, Dij;
    sum = 0;
    for (count = 0, i = 0; i < n - 1; i++) {
        count++;                /* skip diagonal entry */
        for (j = 1; j < n - i; j++, count++) {
            dist = 0;
            neighbor = i + j;
            for (l = 0; l < dim; l++) {
                dist +=
                    (coords[l][i] - coords[l][neighbor]) * (coords[l][i] -
                                                            coords[l]
                                                            [neighbor]);
            }
            dist = sqrt(dist);
            if (exp == 2) {
                Dij = 1.0 / sqrt(lap[count]);
                sum += (Dij - dist) * (Dij - dist) * lap[count];
            } else {
                Dij = 1.0 / lap[count];
                sum += (Dij - dist) * (Dij - dist) * lap[count];
            }
        }
    }
 
    return sum;
}
 
static double
compute_stress1(double **coords, dist_data * distances, int dim, int n, int exp)
{
    /* compute the overall stress */
 
    int i, l, node;
    double sum, dist, Dij;
    sum = 0;
    if (exp == 2) {
        for (i = 0; i < n; i++) {
            for (size_t j = 0; j < distances[i].nedges; j++) {
                node = distances[i].edges[j];
                if (node <= i) {
                    continue;
                }
                dist = 0;
                for (l = 0; l < dim; l++) {
                    dist +=
                        (coords[l][i] - coords[l][node]) * (coords[l][i] -
                                                            coords[l]
                                                            [node]);
                }
                dist = sqrt(dist);
                Dij = distances[i].edist[j];
                sum += (Dij - dist) * (Dij - dist) / (Dij * Dij);
            }
        }
    } else {
        for (i = 0; i < n; i++) {
            for (size_t j = 0; j < distances[i].nedges; j++) {
                node = distances[i].edges[j];
                if (node <= i) {
                    continue;
                }
                dist = 0;
                for (l = 0; l < dim; l++) {
                    dist +=
                        (coords[l][i] - coords[l][node]) * (coords[l][i] -
                                                            coords[l]
                                                            [node]);
                }
                dist = sqrt(dist);
                Dij = distances[i].edist[j];
                sum += (Dij - dist) * (Dij - dist) / Dij;
            }
        }
    }
 
    return sum;
}
 
/* Initialize node coordinates. If the node already has
 * a position, use it.
 * Return true if some node is fixed.
 */
int initLayout(int n, int dim, double **coords, node_t **nodes) {
    node_t *np;
    double *xp;
    double *yp;
    double *pt;
    int i, d;
    int pinned = 0;
 
    xp = coords[0];
    yp = coords[1];
    for (i = 0; i < n; i++) {
        np = nodes[i];
        if (hasPos(np)) {
            pt = ND_pos(np);
            *xp++ = *pt++;
            *yp++ = *pt++;
            if (dim > 2) {
                for (d = 2; d < dim; d++)
                    coords[d][i] = *pt++;
            }
            if (isFixed(np))
                pinned = 1;
        } else {
            *xp++ = drand48();
            *yp++ = drand48();
            if (dim > 2) {
                for (d = 2; d < dim; d++)
                    coords[d][i] = drand48();
            }
        }
    }
 
    for (d = 0; d < dim; d++)
        orthog1(n, coords[d]);
 
    return pinned;
}
 
float *circuitModel(vtx_data * graph, int nG)
{
    int i, j, rv, count;
    float *Dij = gv_calloc(nG * (nG + 1) / 2, sizeof(float));
    double **Gm;
    double **Gm_inv;
 
    Gm = new_array(nG, nG, 0.0);
    Gm_inv = new_array(nG, nG, 0.0);
 
    /* set non-diagonal entries */
    if (graph->ewgts) {
        for (i = 0; i < nG; i++) {
            for (size_t e = 1; e < graph[i].nedges; e++) {
                j = graph[i].edges[e];
                /* conductance is 1/resistance */
                Gm[i][j] = Gm[j][i] = -1.0 / graph[i].ewgts[e]; /* negate */
            }
        }
    } else {
        for (i = 0; i < nG; i++) {
            for (size_t e = 1; e < graph[i].nedges; e++) {
                j = graph[i].edges[e];
                /* conductance is 1/resistance */
                Gm[i][j] = Gm[j][i] = -1.0;     /* ewgts are all 1 */
            }
        }
    }
 
    rv = solveCircuit(nG, Gm, Gm_inv);
 
    if (rv) {
        float v;
        count = 0;
        for (i = 0; i < nG; i++) {
            for (j = i; j < nG; j++) {
                if (i == j)
                    v = 0.0;
                else
                    v = (float) (Gm_inv[i][i] + Gm_inv[j][j] -
                                 2.0 * Gm_inv[i][j]);
                Dij[count++] = v;
            }
        }
    } else {
        free(Dij);
        Dij = NULL;
    }
    free_array(Gm);
    free_array(Gm_inv);
    return Dij;
}
 
/* Optimization of the stress function using sparse distance matrix, within a vector-space
 * Fastest and least accurate method
 *
 * NOTE: We use integral shortest path values here, assuming
 * this is only to get an initial layout. In general, if edge lengths
 * are involved, we may end up with 0 length edges. 
 */
static int sparse_stress_subspace_majorization_kD(vtx_data * graph,     /* Input graph in sparse representation */
                                                  int n,        /* Number of nodes */
                                                  double **coords,      /* coordinates of nodes (output layout)  */
                                                  int dim,      /* dimensionality of layout */
                                                  int smart_ini,        /* smart initialization */
                                                  int exp,      /* scale exponent */
                                                  int reweight_graph,   /* difference model */
                                                  int n_iterations,     /* max #iterations */
                                                  int num_centers       /* #pivots in sparse distance matrix  */
    )
{
    int iterations;             /* output: number of iteration of the process */
 
    double conj_tol = tolerance_cg;     /* tolerance of Conjugate Gradient */
 
        /*************************************************
        ** Computation of pivot-based, sparse, subspace-restricted **
        ** k-D  stress minimization by majorization                **    
        *************************************************/
 
    int i, k, node;
 
        /*************************************************
        ** First compute the subspace in which we optimize     **
        ** The subspace is  the high-dimensional embedding     **
        *************************************************/
 
    int subspace_dim = MIN(stress_pca_dim, n);  /* overall dimensionality of subspace */
    double **subspace = gv_calloc(subspace_dim, sizeof(double *));
    double *d_storage = gv_calloc(subspace_dim * n, sizeof(double));
    int num_centers_local;
    DistType **full_coords;
    /* if i is a pivot than CenterIndex[i] is its index, otherwise CenterIndex[i]= -1 */
    int *invCenterIndex;        /* list the pivot nodes  */
    float *old_weights;
    /* this matrix stores the distance between  each node and each "center" */
    DistType **Dij;
    /* this vector stores the distances of each node to the selected "centers" */
    DistType max_dist;
    DistType *storage;
    int *visited_nodes;
    dist_data *distances;
    int available_space;
    int *storage1 = NULL;
    DistType *storage2 = NULL;
    int num_visited_nodes;
    int num_neighbors;
    int index;
    DistType *dist_list;
    vtx_data *lap;
    int *edges;
    float *ewgts;
    double degree;
    double **directions;
    float **tmp_mat;
    float **matrix;
    double dist_ij;
    double *b;
    double *b_restricted;
    double L_ij;
    double old_stress, new_stress;
    bool converged;
 
    for (i = 0; i < subspace_dim; i++) {
        subspace[i] = d_storage + i * n;
    }
 
    /* compute PHDE: */
    num_centers_local = MIN(n, MAX(2 * subspace_dim, 50));
    full_coords = NULL;
    /* High dimensional embedding */
    embed_graph(graph, n, num_centers_local, &full_coords, reweight_graph);
    /* Centering coordinates */
    center_coordinate(full_coords, n, num_centers_local);
    /* PCA */
    PCA_alloc(full_coords, num_centers_local, n, subspace, subspace_dim);
 
    free(full_coords[0]);
    free(full_coords);
 
        /*************************************************
        ** Compute the sparse-shortest-distances matrix 'distances' **
        *************************************************/
 
    int *CenterIndex = gv_calloc(n, sizeof(int));
    for (i = 0; i < n; i++) {
        CenterIndex[i] = -1;
    }
    invCenterIndex = NULL;
 
    old_weights = graph[0].ewgts;
 
    if (reweight_graph) {
        /* weight graph to separate high-degree nodes */
        /* in the future, perform slower Dijkstra-based computation */
        compute_new_weights(graph, n);
    }
 
    /* compute sparse distance matrix */
    /* first select 'num_centers' pivots from which we compute distance */
    /* to all other nodes */
 
    Dij = NULL;
    DistType *dist = gv_calloc(n, sizeof(DistType));
    if (num_centers == 0) {     /* no pivots, skip pivots-to-nodes distance calculation */
        goto after_pivots_selection;
    }
 
    invCenterIndex = gv_calloc(num_centers, sizeof(int));
 
    storage = gv_calloc(n * num_centers, sizeof(DistType));
    Dij = gv_calloc(num_centers, sizeof(DistType *));
    for (i = 0; i < num_centers; i++)
        Dij[i] = storage + i * n;
 
    // select `num_centers` pivots that are uniformly spread over the graph
 
    /* the first pivots is selected randomly */
    node = rand() % n;
    CenterIndex[node] = 0;
    invCenterIndex[0] = node;
 
    if (reweight_graph) {
        ngdijkstra(node, graph, n, Dij[0]);
    } else {
        bfs(node, graph, n, Dij[0]);
    }
 
    /* find the most distant node from first pivot */
    max_dist = 0;
    for (i = 0; i < n; i++) {
        dist[i] = Dij[0][i];
        if (dist[i] > max_dist) {
            node = i;
            max_dist = dist[i];
        }
    }
    /* select other dim-1 nodes as pivots */
    for (i = 1; i < num_centers; i++) {
        CenterIndex[node] = i;
        invCenterIndex[i] = node;
        if (reweight_graph) {
            ngdijkstra(node, graph, n, Dij[i]);
        } else {
            bfs(node, graph, n, Dij[i]);
        }
        max_dist = 0;
        for (int j = 0; j < n; j++) {
            dist[j] = MIN(dist[j], Dij[i][j]);
            if (dist[j] > max_dist
                || (dist[j] == max_dist && rand() % (j + 1) == 0)) {
                node = j;
                max_dist = dist[j];
            }
        }
    }
 
  after_pivots_selection:
 
    /* Construct a sparse distance matrix 'distances' */
 
    /* initialize dist to -1, important for 'bfs_bounded(..)' */
    for (i = 0; i < n; i++) {
        dist[i] = -1;
    }
 
    visited_nodes = gv_calloc(n, sizeof(int));
    distances = gv_calloc(n, sizeof(dist_data));
    available_space = 0;
    size_t nedges = 0;
    for (i = 0; i < n; i++) {
        if (CenterIndex[i] >= 0) {      /* a pivot node */
            distances[i].edges = gv_calloc(n - 1, sizeof(int));
            distances[i].edist = gv_calloc(n - 1, sizeof(DistType));
            distances[i].nedges = (size_t)n - 1;
            nedges += (size_t)n - 1;
            distances[i].free_mem = true;
            index = CenterIndex[i];
            for (int j = 0; j < i; j++) {
                distances[i].edges[j] = j;
                distances[i].edist[j] = Dij[index][j];
            }
            for (int j = i + 1; j < n; j++) {
                distances[i].edges[j - 1] = j;
                distances[i].edist[j - 1] = Dij[index][j];
            }
            continue;
        }
 
        /* a non pivot node */
 
        num_visited_nodes = 0;
        num_neighbors = num_visited_nodes + num_centers;
        if (num_neighbors > available_space) {
            available_space = n;
            storage1 = gv_calloc(available_space, sizeof(int));
            storage2 = gv_calloc(available_space, sizeof(DistType));
            distances[i].free_mem = true;
        } else {
            distances[i].free_mem = false;
        }
        distances[i].edges = storage1;
        distances[i].edist = storage2;
        distances[i].nedges = (size_t)num_neighbors;
        nedges += (size_t)num_neighbors;
        for (int j = 0; j < num_visited_nodes; j++) {
            storage1[j] = visited_nodes[j];
            storage2[j] = dist[visited_nodes[j]];
            dist[visited_nodes[j]] = -1;
        }
        /* add all pivots: */
        for (int j = num_visited_nodes; j < num_neighbors; j++) {
            index = j - num_visited_nodes;
            storage1[j] = invCenterIndex[index];
            storage2[j] = Dij[index][i];
        }
 
        storage1 += num_neighbors;
        storage2 += num_neighbors;
        available_space -= num_neighbors;
    }
 
    free(dist);
    free(visited_nodes);
 
    if (Dij != NULL) {
        free(Dij[0]);
        free(Dij);
    }
 
        /*************************************************
        ** Laplacian computation **
        *************************************************/
 
    lap = gv_calloc(n, sizeof(vtx_data));
    edges = gv_calloc(nedges + n, sizeof(int));
    ewgts = gv_calloc(nedges + n, sizeof(float));
    for (i = 0; i < n; i++) {
        lap[i].edges = edges;
        lap[i].ewgts = ewgts;
        lap[i].nedges = distances[i].nedges + 1;        /*add the self loop */
        dist_list = distances[i].edist - 1;     /* '-1' since edist[0] goes for number '1' entry in the lap */
        degree = 0;
        if (exp == 2) {
            for (size_t j = 1; j < lap[i].nedges; j++) {
                edges[j] = distances[i].edges[j - 1];
                ewgts[j] = (float) -1.0 / ((float) dist_list[j] * (float) dist_list[j]);        /* cast to float to prevent overflow */
                degree -= ewgts[j];
            }
        } else {
            for (size_t j = 1; j < lap[i].nedges; j++) {
                edges[j] = distances[i].edges[j - 1];
                ewgts[j] = -1.0f / (float) dist_list[j];
                degree -= ewgts[j];
            }
        }
        edges[0] = i;
        ewgts[0] = (float) degree;
        edges += lap[i].nedges;
        ewgts += lap[i].nedges;
    }
 
        /*************************************************
        ** initialize direction vectors  **
        ** to get an initial layout       **
        *************************************************/
 
    /* the layout is subspace*directions */
    directions = gv_calloc(dim, sizeof(double *));
    directions[0] = gv_calloc(dim * subspace_dim, sizeof(double));
    for (i = 1; i < dim; i++) {
        directions[i] = directions[0] + i * subspace_dim;
    }
 
    if (smart_ini) {
        /* smart initialization */
        for (k = 0; k < dim; k++) {
            for (i = 0; i < subspace_dim; i++) {
                directions[k][i] = 0;
            }
        }
        if (dim != 2) {
            /* use the first vectors in the eigenspace */
            /* each direction points to its "principal axes" */
            for (k = 0; k < dim; k++) {
                directions[k][k] = 1;
            }
        } else {
            /* for the frequent 2-D case we prefer iterative-PCA over PCA */
            /* Note that we don't want to mix the Lap's eigenspace with the HDE */
            /* in the computation since they have different scales */
 
            directions[0][0] = 1;       /* first pca projection vector */
            if (!iterativePCA_1D(subspace, subspace_dim, n, directions[1])) {
                for (k = 0; k < subspace_dim; k++) {
                    directions[1][k] = 0;
                }
                directions[1][1] = 1;
            }
        }
 
    } else {
        /* random initialization */
        for (k = 0; k < dim; k++) {
            for (i = 0; i < subspace_dim; i++) {
                directions[k][i] = (double) rand() / RAND_MAX;
            }
        }
    }
 
    /* compute initial k-D layout */
 
    for (k = 0; k < dim; k++) {
        right_mult_with_vector_transpose(subspace, n, subspace_dim,
                                         directions[k], coords[k]);
    }
 
        /*************************************************
        ** compute restriction of the laplacian to subspace: ** 
        *************************************************/
 
    tmp_mat = NULL;
    matrix = NULL;
    mult_sparse_dense_mat_transpose(lap, subspace, n, subspace_dim,
                                    &tmp_mat);
    mult_dense_mat(subspace, tmp_mat, subspace_dim, n, subspace_dim,
                   &matrix);
    free(tmp_mat[0]);
    free(tmp_mat);
 
        /*************************************************
        ** Layout optimization  **
        *************************************************/
 
    b = gv_calloc(n, sizeof(double));
    b_restricted = gv_calloc(subspace_dim, sizeof(double));
    old_stress = compute_stress1(coords, distances, dim, n, exp);
    for (converged = false, iterations = 0;
         iterations < n_iterations && !converged; iterations++) {
 
        /* Axis-by-axis optimization: */
        for (k = 0; k < dim; k++) {
            /* compute the vector b */
            /* multiply on-the-fly with distance-based laplacian */
            /* (for saving storage we don't construct this Lap explicitly) */
            for (i = 0; i < n; i++) {
                degree = 0;
                b[i] = 0;
                dist_list = distances[i].edist - 1;
                edges = lap[i].edges;
                ewgts = lap[i].ewgts;
                for (size_t j = 1; j < lap[i].nedges; j++) {
                    node = edges[j];
                    dist_ij = distance_kD(coords, dim, i, node);
                    if (dist_ij > 1e-30) {      /* skip zero distances */
                        L_ij = -ewgts[j] * dist_list[j] / dist_ij;      /* L_ij=w_{ij}*d_{ij}/dist_{ij} */
                        degree -= L_ij;
                        b[i] += L_ij * coords[k][node];
                    }
                }
                b[i] += degree * coords[k][i];
            }
            right_mult_with_vector_d(subspace, subspace_dim, n, b,
                                     b_restricted);
            if (conjugate_gradient_f(matrix, directions[k], b_restricted,
                                 subspace_dim, conj_tol, subspace_dim,
                                 false)) {
                iterations = -1;
                goto finish0;
            }
            right_mult_with_vector_transpose(subspace, n, subspace_dim,
                                             directions[k], coords[k]);
        }
 
        if (iterations % 2 == 0) { // check for convergence each two iterations
            new_stress = compute_stress1(coords, distances, dim, n, exp);
            converged = fabs(new_stress - old_stress) / (new_stress + 1e-10) < Epsilon;
            old_stress = new_stress;
        }
    }
finish0:
    free(b_restricted);
    free(b);
 
    if (reweight_graph) {
        restore_old_weights(graph, n, old_weights);
    }
 
    for (i = 0; i < n; i++) {
        if (distances[i].free_mem) {
            free(distances[i].edges);
            free(distances[i].edist);
        }
    }
 
    free(distances);
    free(lap[0].edges);
    free(lap[0].ewgts);
    free(lap);
    free(CenterIndex);
    free(invCenterIndex);
    free(directions[0]);
    free(directions);
    if (matrix != NULL) {
        free(matrix[0]);
        free(matrix);
    }
    free(subspace[0]);
    free(subspace);
 
    return iterations;
}
 
/* compute_weighted_apsp_packed:
 * Edge lengths can be any float > 0
 */
static float *compute_weighted_apsp_packed(vtx_data * graph, int n)
{
    int i, j, count;
    float *Dij = gv_calloc(n * (n + 1) / 2, sizeof(float));
 
    float *Di = gv_calloc(n, sizeof(float));
 
    count = 0;
    for (i = 0; i < n; i++) {
        dijkstra_f(i, graph, n, Di);
        for (j = i; j < n; j++) {
            Dij[count++] = Di[j];
        }
    }
    free(Di);
    return Dij;
}
 
 
float *mdsModel(vtx_data * graph, int nG)
{
    int i, j;
    float *Dij;
    int shift = 0;
    double delta = 0.0;
 
    if (graph->ewgts == NULL)
        return 0;
 
    /* first, compute shortest paths to fill in non-edges */
    Dij = compute_weighted_apsp_packed(graph, nG);
 
    /* then, replace edge entries will user-supplied len */
    for (i = 0; i < nG; i++) {
        shift += i;
        for (size_t e = 1; e < graph[i].nedges; e++) {
            j = graph[i].edges[e];
            if (j < i)
                continue;
            delta += fabsf(Dij[i * nG + j - shift] - graph[i].ewgts[e]);
            Dij[i * nG + j - shift] = graph[i].ewgts[e];
        }
    }
    if (Verbose) {
        fprintf(stderr, "mdsModel: delta = %f\n", delta);
    }
    return Dij;
}
 
float *compute_apsp_packed(vtx_data * graph, int n)
{
    int i, j, count;
    float *Dij = gv_calloc(n * (n + 1) / 2, sizeof(float));
 
    DistType *Di = gv_calloc(n, sizeof(DistType));
 
    count = 0;
    for (i = 0; i < n; i++) {
        bfs(i, graph, n, Di);
        for (j = i; j < n; j++) {
            Dij[count++] = (float)Di[j];
        }
    }
    free(Di);
    return Dij;
}
 
float *compute_apsp_artificial_weights_packed(vtx_data *graph, int n) {
    /* compute all-pairs-shortest-path-length while weighting the graph */
    /* so high-degree nodes are distantly located */
 
    float *Dij;
    int i;
    float *old_weights = graph[0].ewgts;
    size_t nedges = 0;
    size_t deg_i, deg_j;
    int neighbor;
 
    for (i = 0; i < n; i++) {
        nedges += graph[i].nedges;
    }
 
    float *weights = gv_calloc(nedges, sizeof(float));
    int *vtx_vec = gv_calloc(n, sizeof(int));
 
    if (graph->ewgts) {
        for (i = 0; i < n; i++) {
            fill_neighbors_vec_unweighted(graph, i, vtx_vec);
            deg_i = graph[i].nedges - 1;
            for (size_t j = 1; j <= deg_i; j++) {
                neighbor = graph[i].edges[j];
                deg_j = graph[neighbor].nedges - 1;
                weights[j] = fmaxf((float)(deg_i + deg_j -
                         2 * common_neighbors(graph, neighbor, vtx_vec)), graph[i].ewgts[j]);
            }
            empty_neighbors_vec(graph, i, vtx_vec);
            graph[i].ewgts = weights;
            weights += graph[i].nedges;
        }
        Dij = compute_weighted_apsp_packed(graph, n);
    } else {
        for (i = 0; i < n; i++) {
            graph[i].ewgts = weights;
            fill_neighbors_vec_unweighted(graph, i, vtx_vec);
            deg_i = graph[i].nedges - 1;
            for (size_t j = 1; j <= deg_i; j++) {
                neighbor = graph[i].edges[j];
                deg_j = graph[neighbor].nedges - 1;
                weights[j] =
                    (float)(deg_i + deg_j - 2 * common_neighbors(graph, neighbor, vtx_vec));
            }
            empty_neighbors_vec(graph, i, vtx_vec);
            weights += graph[i].nedges;
        }
        Dij = compute_apsp_packed(graph, n);
    }
 
    free(vtx_vec);
    free(graph[0].ewgts);
    graph[0].ewgts = NULL;
    if (old_weights != NULL) {
        for (i = 0; i < n; i++) {
            graph[i].ewgts = old_weights;
            old_weights += graph[i].nedges;
        }
    }
    return Dij;
}
 
/* Accumulator type for diagonal of Laplacian. Needs to be as large
 * as possible. Use long double; configure to double if necessary.
 */
#define DegType long double
 
int stress_majorization_kD_mkernel(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,   /* model */
                                   int maxi     /* max iterations */
    )
{
    int iterations;             /* output: number of iteration of the process */
 
    double conj_tol = tolerance_cg;     /* tolerance of Conjugate Gradient */
    float *Dij = NULL;
    int i, j, k;
    float **coords = NULL;
    float *f_storage = NULL;
    float constant_term;
    int count;
    DegType degree;
    int lap_length;
    float *lap2 = NULL;
    DegType *degrees = NULL;
    int step;
    float val;
    double old_stress, new_stress;
    bool converged;
    float **b = NULL;
    float *tmp_coords = NULL;
    float *dist_accumulator = NULL;
    float *lap1 = NULL;
    int smart_ini = opts & opt_smart_init;
    int exp = opts & opt_exp_flag;
    int len;
    int havePinned;             /* some node is pinned */
 
        /*************************************************
        ** Computation of full, dense, unrestricted k-D ** 
        ** stress minimization by majorization          **    
        *************************************************/
 
        /****************************************************
        ** Compute the all-pairs-shortest-distances matrix **
        ****************************************************/
 
    if (maxi < 0)
        return 0;
 
    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");
        if (graph->ewgts)
            Dij = compute_weighted_apsp_packed(graph, n);
        else
            Dij = compute_apsp_packed(graph, n);
    }
 
    if (Verbose) {
        fprintf(stderr, ": %.2f sec\n", elapsed_sec());
        fprintf(stderr, "Setting initial positions");
        start_timer();
    }
 
        /**************************
        ** Layout initialization **
        **************************/
 
    if (smart_ini && n > 1) {
        havePinned = 0;
        /* optimize layout quickly within subspace */
        /* perform at most 50 iterations within 30-D subspace to 
           get an estimate */
        if (sparse_stress_subspace_majorization_kD(graph, n,
                                               d_coords, dim, smart_ini, exp,
                                               model == MODEL_SUBSET, 50,
                                               num_pivots_stress) < 0) {
            iterations = -1;
            goto finish1;
        }
 
        for (i = 0; i < dim; i++) {
            /* for numerical stability, scale down layout */
            double max = 1;
            for (j = 0; j < n; j++) {
                if (fabs(d_coords[i][j]) > max) {
                    max = fabs(d_coords[i][j]);
                }
            }
            for (j = 0; j < n; j++) {
                d_coords[i][j] /= max;
            }
            /* add small random noise */
            for (j = 0; j < n; j++) {
                d_coords[i][j] += 1e-6 * (drand48() - 0.5);
            }
            orthog1(n, d_coords[i]);
        }
    } else {
        havePinned = initLayout(n, dim, d_coords, nodes);
    }
    if (Verbose)
        fprintf(stderr, ": %.2f sec", elapsed_sec());
    if (n == 1 || maxi == 0) {
        free(Dij);
        return 0;
    }
 
    if (Verbose) {
        fprintf(stderr, ": %.2f sec\n", elapsed_sec());
        fprintf(stderr, "Setting up stress function");
        start_timer();
    }
    coords = gv_calloc(dim, sizeof(float *));
    f_storage = gv_calloc(dim * n, sizeof(float));
    for (i = 0; i < dim; i++) {
        coords[i] = f_storage + i * n;
        for (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 */
    assert(exp == 1 || exp == 2);
    constant_term = (float)n * (n - 1) / 2;
 
        /**************************
        ** Laplacian computation **
        **************************/
 
    lap_length = n * (n + 1) / 2;
    lap2 = Dij;
    if (exp == 2) {
    square_vec(lap_length, lap2);
    }
    /* compute off-diagonal entries */
    invert_vec(lap_length, lap2);
 
    /* compute diagonal entries */
    count = 0;
    degrees = gv_calloc(n, sizeof(DegType));
    for (i = 0; i < n - 1; i++) {
        degree = 0;
        count++;                /* skip main diag entry */
        for (j = 1; j < n - i; j++, count++) {
            val = lap2[count];
            degree += val;
            degrees[i + j] -= val;
        }
        degrees[i] -= degree;
    }
    for (step = n, count = 0, i = 0; i < n; i++, count += step, step--) {
        lap2[count] = degrees[i];
    }
 
        /*************************
        ** Layout optimization  **
        *************************/
 
    b = gv_calloc(dim, sizeof(float *));
    b[0] = gv_calloc(dim * n, sizeof(float));
    for (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));
 
 
    old_stress = DBL_MAX; // at least one iteration
    if (Verbose) {
        fprintf(stderr, ": %.2f sec\n", elapsed_sec());
        fprintf(stderr, "Solving model: ");
        start_timer();
    }
 
    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); */
        memset(degrees, 0, n * sizeof(DegType));
        if (exp == 2) {
            sqrt_vecf(lap_length, lap2, lap1);
        }
        for (count = 0, i = 0; i < n - 1; i++) {
            len = n - i - 1;
            /* init 'dist_accumulator' with zeros */
            set_vector_valf(len, 0, dist_accumulator);
 
            /* put into 'dist_accumulator' all squared distances between 'i' and 'i'+1,...,'n'-1 */
            for (k = 0; k < dim; k++) {
                size_t x;
                for (x = 0; x < (size_t)len; ++x) {
                    float tmp = coords[k][i] + -1.0f * (coords[k] + i + 1)[x];
                    dist_accumulator[x] += tmp * tmp;
                }
            }
 
            /* convert to 1/d_{ij} */
            invert_sqrt_vec(len, dist_accumulator);
            /* detect overflows */
            for (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 */
            degree = 0;
            if (exp == 2) {
                for (j = 0; j < len; j++, count++) {
                    val = lap1[count] *= dist_accumulator[j];
                    degree += val;
                    degrees[i + j + 1] -= val;
                }
            } else {
                for (j = 0; j < len; j++, count++) {
                    val = lap1[count] = dist_accumulator[j];
                    degree += val;
                    degrees[i + j + 1] -= val;
                }
            }
            degrees[i] -= degree;
        }
        for (step = n, count = 0, i = 0; i < n; i++, count += step, step--) {
            lap1[count] = degrees[i];
        }
 
        /* Now compute b[] */
        for (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 */
        new_stress = 0;
        for (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 (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);
        }
        /* Invariant: old_stress > 0. In theory, old_stress >= new_stress
         * but we use fabs in case of numerical error.
         */
        {
            double diff = old_stress - new_stress;
            double change = fabs(diff);
            converged = change / old_stress < Epsilon || new_stress < Epsilon;
        }
        old_stress = new_stress;
 
        for (k = 0; k < dim; k++) {
            node_t *np;
            if (havePinned) {
                copy_vectorf(n, coords[k], tmp_coords);
                if (conjugate_gradient_mkernel(lap2, tmp_coords, b[k], n,
                                           conj_tol, n) < 0) {
                    iterations = -1;
                    goto finish1;
                }
                for (i = 0; i < n; i++) {
                    np = nodes[i];
                    if (!isFixed(np))
                        coords[k][i] = tmp_coords[i];
                }
            } else {
                if (conjugate_gradient_mkernel(lap2, coords[k], b[k], n,
                                           conj_tol, n) < 0) {
                    iterations = -1;
                    goto finish1;
                }
            }
        }
        if (Verbose && iterations % 5 == 0) {
            fprintf(stderr, "%.3f ", new_stress);
            if ((iterations + 5) % 50 == 0)
                fprintf(stderr, "\n");
        }
    }
    if (Verbose) {
        fprintf(stderr, "\nfinal e = %f %d iterations %.2f sec\n",
                compute_stressf(coords, lap2, dim, n, exp),
                iterations, elapsed_sec());
    }
 
    for (i = 0; i < dim; i++) {
        for (j = 0; j < n; j++) {
            d_coords[i][j] = coords[i][j];
        }
    }
finish1:
    free(f_storage);
    free(coords);
 
    free(lap2);
    if (b) {
        free(b[0]);
        free(b);
    }
    free(tmp_coords);
    free(dist_accumulator);
    free(degrees);
    free(lap1);
    return iterations;
}