Yuval Tassa
edbdb5195c
PiperOrigin-RevId: 813754244 Change-Id: I6836e41c3b021cb727e922c25c60f629b9814c93
1398 lines
38 KiB
C
1398 lines
38 KiB
C
// Copyright 2021 DeepMind Technologies Limited
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "engine/engine_util_solve.h"
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#include <stdio.h>
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#include <mujoco/mjdata.h>
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#include <mujoco/mjmacro.h>
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#include <mujoco/mjsan.h> // IWYU pragma: keep
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#include "engine/engine_util_blas.h"
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#include "engine/engine_util_errmem.h"
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#include "engine/engine_util_misc.h"
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#include "engine/engine_util_sparse.h"
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#include "engine/engine_memory.h"
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#include "engine/engine_util_spatial.h"
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//---------------------------- dense Cholesky ------------------------------------------------------
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// Cholesky decomposition: mat = L*L'; return 'rank'
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int mju_cholFactor(mjtNum* mat, int n, mjtNum mindiag) {
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int rank = n;
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mjtNum tmp;
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// in-place Cholesky factorization
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for (int j=0; j < n; j++) {
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// compute new diagonal
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tmp = mat[j*(n+1)];
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if (j) {
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tmp -= mju_dot(mat+j*n, mat+j*n, j);
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}
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// correct diagonal values below threshold
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if (tmp < mindiag) {
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tmp = mindiag;
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rank--;
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}
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// save diagonal
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mat[j*(n+1)] = mju_sqrt(tmp);
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// process off-diagonal entries
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tmp = 1/mat[j*(n+1)];
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for (int i=j+1; i < n; i++) {
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mat[i*n+j] = (mat[i*n+j] - mju_dot(mat+i*n, mat+j*n, j)) * tmp;
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}
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}
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return rank;
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}
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// Cholesky solve
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void mju_cholSolve(mjtNum* res, const mjtNum* mat, const mjtNum* vec, int n) {
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// copy if source and destination are different
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if (res != vec) {
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mju_copy(res, vec, n);
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}
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// forward substitution: solve L*res = vec
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for (int i=0; i < n; i++) {
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if (i) {
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res[i] -= mju_dot(mat+i*n, res, i);
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}
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// diagonal
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res[i] /= mat[i*(n+1)];
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}
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// backward substitution: solve L'*res = res
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for (int i=n-1; i >= 0; i--) {
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if (i < n-1) {
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for (int j=i+1; j < n; j++) {
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res[i] -= mat[j*n+i] * res[j];
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}
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}
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// diagonal
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res[i] /= mat[i*(n+1)];
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}
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}
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// Cholesky rank-one update: L*L' +/- x*x'; return rank
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int mju_cholUpdate(mjtNum* mat, mjtNum* x, int n, int flg_plus) {
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int rank = n;
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mjtNum r, c, cinv, s, Lkk, tmp;
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for (int k=0; k < n; k++) {
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if (x[k]) {
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// prepare constants
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Lkk = mat[k*(n+1)];
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tmp = Lkk*Lkk + (flg_plus ? x[k]*x[k] : -x[k]*x[k]);
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if (tmp < mjMINVAL) {
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tmp = mjMINVAL;
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rank--;
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}
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r = mju_sqrt(tmp);
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c = r / Lkk;
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cinv = 1 / c;
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s = x[k] / Lkk;
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// update diagonal
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mat[k*(n+1)] = r;
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// update mat
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if (flg_plus) {
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for (int i=k+1; i < n; i++) {
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mat[i*n+k] = (mat[i*n+k] + s*x[i])*cinv;
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}
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} else {
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for (int i=k+1; i < n; i++) {
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mat[i*n+k] = (mat[i*n+k] - s*x[i])*cinv;
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}
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}
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// update x
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for (int i=k+1; i < n; i++) {
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x[i] = c*x[i] - s*mat[i*n+k];
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}
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}
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}
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return rank;
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}
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//---------------------------- sparse Cholesky -----------------------------------------------------
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// sparse reverse-order Cholesky decomposition: mat = L'*L; return 'rank'
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// mat must be lower-triangular, have preallocated space for fill-in
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int mju_cholFactorSparse(mjtNum* mat, int n, mjtNum mindiag,
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int* rownnz, const int* rowadr, int* colind,
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mjData* d) {
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int rank = n;
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mj_markStack(d);
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mjtNum* buf = mjSTACKALLOC(d, n, mjtNum);
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int* buf_ind = mjSTACKALLOC(d, n, int);
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// backpass over rows
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for (int r=n-1; r >= 0; r--) {
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// get rownnz and rowadr for row r
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int nnz = rownnz[r], adr = rowadr[r];
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// update row r diagonal
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mjtNum tmp = mat[adr+nnz-1];
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if (tmp < mindiag) {
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tmp = mindiag;
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rank--;
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}
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mat[adr+nnz-1] = mju_sqrt(tmp);
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tmp = 1/mat[adr+nnz-1];
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// update row r before diagonal
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for (int i=0; i < nnz-1; i++) {
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mat[adr+i] *= tmp;
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}
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// update row c<r where mat(r,c)!=0
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for (int i=0; i < nnz-1; i++) {
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// get column index
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int c = colind[adr+i];
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// mat(c,0:c) = mat(c,0:c) - mat(r,c) * mat(r,0:c)
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int nnz_c = mju_combineSparse(mat + rowadr[c], mat+rowadr[r], 1, -mat[adr+i],
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rownnz[c], i+1, colind+rowadr[c], colind+rowadr[r],
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buf, buf_ind);
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// assign new nnz to row c
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rownnz[c] = nnz_c;
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}
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}
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mj_freeStack(d);
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return rank;
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}
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// precount row non-zeros of reverse-Cholesky factor L, return total non-zeros
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// based on ldl_symbolic from 'Algorithm 8xx: a concise sparse Cholesky factorization package'
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// reads pattern from upper triangle
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int mju_cholFactorCount(int* L_rownnz, const int* rownnz, const int* rowadr, const int* colind,
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int n, mjData* d) {
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mj_markStack(d);
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int* parent = mjSTACKALLOC(d, n, int);
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int* flag = mjSTACKALLOC(d, n, int);
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// loop over rows in reverse order
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for (int r = n - 1; r >= 0; r--) {
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parent[r] = -1;
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flag[r] = r;
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L_rownnz[r] = 1; // start with 1 for diagonal
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// loop over non-zero columns of upper triangle
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int start = rowadr[r];
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int end = start + rownnz[r];
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for (int c = start; c < end; c++) {
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int i = colind[c];
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// skip lower triangle
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if (i <= r) {
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continue;
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}
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// traverse from i to ancestor, stop when row is flagged
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while (flag[i] != r) {
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// if not yet set, set parent to current row
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if (parent[i] == -1) {
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parent[i] = r;
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}
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// increment non-zeros, flag row i, advance to parent
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L_rownnz[i]++;
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flag[i] = r;
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i = parent[i];
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}
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}
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}
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mj_freeStack(d);
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// sum up all row non-zeros
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int nnz = 0;
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for (int r = 0; r < n; r++) {
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nnz += L_rownnz[r];
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}
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return nnz;
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}
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// sparse reverse-order Cholesky solve
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void mju_cholSolveSparse(mjtNum* res, const mjtNum* mat, const mjtNum* vec, int n,
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const int* rownnz, const int* rowadr, const int* colind) {
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// copy input into result
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mju_copy(res, vec, n);
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// vec <- L^-T vec
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for (int i=n-1; i >= 0; i--) {
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if (res[i]) {
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// get rowadr[i], rownnz[i]
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const int adr = rowadr[i], nnz = rownnz[i];
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// x(i) /= L(i,i)
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res[i] /= mat[adr+nnz-1];
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mjtNum tmp = res[i];
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// x(j) -= L(i,j)*x(i), j=0:i-1
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for (int j=0; j < nnz-1; j++) {
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res[colind[adr+j]] -= mat[adr+j]*tmp;
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}
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}
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}
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// vec <- L^-1 vec
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for (int i=0; i < n; i++) {
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// get rowadr[i], rownnz[i]
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const int adr = rowadr[i], nnz = rownnz[i];
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// x(i) -= sum_j L(i,j)*x(j), j=0:i-1
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if (nnz > 1) {
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res[i] -= mju_dotSparse(mat+adr, res, nnz-1, colind+adr);
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// modulo AVX, the above line does
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// for (int j=0; j<nnz-1; j++)
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// res[i] -= mat[adr+j]*res[colind[adr+j]];
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}
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// x(i) /= L(i,i)
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res[i] /= mat[adr+nnz-1];
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}
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}
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// sparse reverse-order Cholesky rank-one update: L'*L +/- x*x'; return rank
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// x is sparse, change in sparsity pattern of mat is not allowed
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int mju_cholUpdateSparse(mjtNum* mat, mjtNum* x, int n, int flg_plus,
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const int* rownnz, const int* rowadr, const int* colind,
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int x_nnz, int* x_ind,
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mjData* d) {
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mj_markStack(d);
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int* buf_ind = mjSTACKALLOC(d, n, int);
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mjtNum* sparse_buf = mjSTACKALLOC(d, n, mjtNum);
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// backpass over rows corresponding to non-zero x(r)
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int rank = n, i = x_nnz - 1;
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while (i >= 0) {
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// get rownnz and rowadr for this row
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int nnz = rownnz[x_ind[i]], adr = rowadr[x_ind[i]];
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// compute quantities
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mjtNum tmp = mat[adr+nnz-1]*mat[adr+nnz-1] + (flg_plus ? x[i]*x[i] : -x[i]*x[i]);
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if (tmp < mjMINVAL) {
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tmp = mjMINVAL;
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rank--;
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}
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mjtNum r = mju_sqrt(tmp);
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mjtNum c = r / mat[adr+nnz-1];
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mjtNum s = x[i] / mat[adr+nnz-1];
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// update diagonal
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mat[adr+nnz-1] = r;
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// update row: mat(r,1:r-1) = (mat(r,1:r-1) + s*x(1:r-1)) / c
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mju_combineSparseInc(mat + adr, x, n, 1 / c, (flg_plus ? s / c : -s / c),
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nnz-1, i, colind + adr, x_ind);
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// update x: x(1:r-1) = c*x(1:r-1) - s*mat(r,1:r-1)
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int new_x_nnz = mju_combineSparse(x, mat+adr, c, -s, i, nnz-1, x_ind,
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colind+adr, sparse_buf, buf_ind);
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// update i, correct for changing x
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i = i - 1 + (new_x_nnz - i);
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}
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mj_freeStack(d);
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return rank;
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}
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//---------------------------- banded Cholesky -----------------------------------------------------
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// band-dense Cholesky decomposition
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// returns minimum value in the factorized diagonal, or 0 if rank-deficient
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// mat has (ntotal-ndense) x nband + ndense x ntotal elements
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// the first (ntotal-ndense) x nband store the band part, left of diagonal, inclusive
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// the second ndense x ntotal store the band part as entire dense rows
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// add diagadd+diagmul*mat_ii to diagonal before factorization
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mjtNum mju_cholFactorBand(mjtNum* mat, int ntotal, int nband, int ndense,
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mjtNum diagadd, mjtNum diagmul) {
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int nsparse = ntotal - ndense;
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mjtNum mindiag = -1;
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// sparse part, including sparse-sparse and sparse-dense
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for (int j=0; j < nsparse; j++) {
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// number of non-zeros left of (j,j)
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int width_jj = mjMIN(j, nband-1);
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// number of non-zeros below (j,j), sparse part
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int height = mjMIN(nsparse-j-1, nband-1);
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// address of (j,j)
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int adr_jj = (j+1)*nband-1;
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// compute L(j,j), before sqrt
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mjtNum left_ij = width_jj > 0 ? mju_dot(mat+adr_jj-width_jj, mat+adr_jj-width_jj, width_jj) : 0;
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mjtNum Ljj = diagadd + diagmul*mat[adr_jj] + mat[adr_jj] - left_ij;
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// update mindiag
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if (Ljj < mindiag || mindiag < 0) {
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mindiag = Ljj;
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}
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// stop if rank-deficient
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if (Ljj < mjMINVAL) {
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return 0;
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}
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// compute Ljj, scale = 1/Ljj
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Ljj = mju_sqrt(Ljj);
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mjtNum scale = 1/Ljj;
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// compute L(i,j) for i>j, sparse part
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for (int i=j+1; i <= j+height; i++) {
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// number of non-zeros left of (i,j)
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int width_ij = mjMIN(j, nband-1-i+j);
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// address of (i,j)
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int adr_ij = (i+1)*nband-1-i+j;
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// in-place computation of L(i,j)
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left_ij = width_ij > 0 ? mju_dot(mat+adr_jj-width_ij, mat+adr_ij-width_ij, width_ij) : 0;
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mat[adr_ij] = scale * (mat[adr_ij] - left_ij);
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}
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// compute L(i,j) for i>j, dense part
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for (int i=nsparse; i < ntotal; i++) {
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// address of (i,j)
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int adr_ij = nsparse*nband + (i-nsparse)*ntotal + j;
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// in-place computation of L(i,j)
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// number of non-zeros left of (i,j) now equals width_jj
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left_ij = width_jj > 0 ? mju_dot(mat+adr_jj-width_jj, mat+adr_ij-width_jj, width_jj) : 0;
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mat[adr_ij] = scale * (mat[adr_ij] - left_ij);
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}
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// save L(j,j)
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mat[adr_jj] = Ljj;
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}
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// dense part
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for (int j=nsparse; j < ntotal; j++) {
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// address of (j,j)
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int adr_jj = nsparse*nband + (j-nsparse)*ntotal + j;
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// compute Ljj
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mjtNum Ljj = diagadd + diagmul*mat[adr_jj] + mat[adr_jj] -
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mju_dot(mat+adr_jj-j, mat+adr_jj-j, j);
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// update mindiag
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if (Ljj < mindiag || mindiag < 0) {
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mindiag = Ljj;
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}
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// stop if rank-deficient
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if (Ljj < mjMINVAL) {
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return 0;
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}
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// compute Ljj, scale = 1/Ljj
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Ljj = mju_sqrt(Ljj);
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mjtNum scale = 1/Ljj;
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// compute L(i,j) for i>j
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for (int i=j+1; i < ntotal; i++) {
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// address of off-diagonal element
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int adr_ij = adr_jj + ntotal*(i-j);
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// in-place computation of L(i,j)
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mat[adr_ij] = scale * (mat[adr_ij] - mju_dot(mat+adr_jj-j, mat+adr_ij-j, j));
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}
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// save L(j,j)
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mat[adr_jj] = Ljj;
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}
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return mindiag;
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}
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// solve with band-Cholesky decomposition
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void mju_cholSolveBand(mjtNum* res, const mjtNum* mat, const mjtNum* vec,
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int ntotal, int nband, int ndense) {
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int width, height, nsparse = ntotal - ndense;
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// copy into result if different
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if (res != vec) {
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mju_copy(res, vec, ntotal);
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}
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//------- forward substitution: solve L*res = vec
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// sparse part
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for (int i=0; i < nsparse; i++) {
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// number of non-zeros left of (i,i)
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width = mjMIN(i, nband-1);
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if (width) {
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res[i] -= mju_dot(mat+(i+1)*nband-1-width, res+i-width, width);
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}
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// diagonal
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res[i] /= mat[(i+1)*nband-1];
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}
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// dense part
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for (int i=nsparse; i < ntotal; i++) {
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res[i] -= mju_dot(mat+nsparse*nband+(i-nsparse)*ntotal, res, i);
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// diagonal
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res[i] /= mat[nsparse*nband+(i-nsparse)*ntotal+i];
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}
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//------- backward substitution: solve L'*res = res
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// dense part
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for (int i=ntotal-1; i >= nsparse; i--) {
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for (int j=i+1; j < ntotal; j++) {
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|
res[i] -= mat[nsparse*nband+(j-nsparse)*ntotal+i] * res[j];
|
|
}
|
|
|
|
// diagonal
|
|
res[i] /= mat[nsparse*nband+(i-nsparse)*ntotal+i];
|
|
}
|
|
|
|
// sparse part
|
|
for (int i=nsparse-1; i >= 0; i--) {
|
|
// number of non-zeros below (i,i), sparse part
|
|
height = mjMIN(nsparse-1-i, nband-1);
|
|
|
|
// sparse rows
|
|
for (int j=i+1; j <= i+height; j++)
|
|
res[i] -= mat[(j+1)*nband-1-(j-i)] * res[j];
|
|
|
|
// dense rows
|
|
for (int j=nsparse; j < ntotal; j++)
|
|
res[i] -= mat[nsparse*nband+(j-nsparse)*ntotal+i] * res[j];
|
|
|
|
// diagonal
|
|
res[i] /= mat[(i+1)*nband-1];
|
|
}
|
|
}
|
|
|
|
|
|
// address of diagonal element i in band-dense matrix representation
|
|
int mju_bandDiag(int i, int ntotal, int nband, int ndense) {
|
|
int nsparse = ntotal-ndense;
|
|
|
|
// sparse part
|
|
if (i < nsparse) {
|
|
return i*nband + nband-1;
|
|
}
|
|
|
|
// dense part
|
|
else {
|
|
return nsparse*nband + (i-nsparse)*ntotal + i;
|
|
}
|
|
}
|
|
|
|
|
|
// convert band matrix to dense matrix
|
|
void mju_band2Dense(mjtNum* res, const mjtNum* mat, int ntotal, int nband, int ndense,
|
|
mjtByte flg_sym) {
|
|
int nsparse = ntotal-ndense;
|
|
|
|
// clear all
|
|
mju_zero(res, ntotal*ntotal);
|
|
|
|
// sparse part
|
|
for(int i=0; i < nsparse; i++) {
|
|
// number of non-zeros left of (i,i)
|
|
int width = mjMIN(i, nband-1);
|
|
|
|
// copy data
|
|
mju_copy(res + i*ntotal + i-width, mat + (i+1)*nband - (width+1), width+1);
|
|
}
|
|
|
|
// dense part
|
|
for(int i=nsparse; i < ntotal; i++) {
|
|
mju_copy(res + i*ntotal, mat + nsparse*nband + (i-nsparse)*ntotal, i+1);
|
|
}
|
|
|
|
// make symmetric
|
|
if (flg_sym) {
|
|
for(int i=0; i < ntotal; i++) {
|
|
for (int j=i+1; j < ntotal; j++) {
|
|
res[i*ntotal + j] = res[j*ntotal + i];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
// convert dense matrix to band matrix
|
|
void mju_dense2Band(mjtNum* res, const mjtNum* mat, int ntotal, int nband, int ndense) {
|
|
int nsparse = ntotal-ndense;
|
|
|
|
// sparse part
|
|
for(int i=0; i < nsparse; i++) {
|
|
// number of non-zeros left of (i,i)
|
|
int width = mjMIN(i, nband-1);
|
|
|
|
// copy data
|
|
mju_copy(res + (i+1)*nband - (width+1), mat + i*ntotal + i-width, width+1);
|
|
}
|
|
|
|
// dense part
|
|
for(int i=nsparse; i < ntotal; i++) {
|
|
mju_copy(res + nsparse*nband + (i-nsparse)*ntotal, mat + i*ntotal, i+1);
|
|
}
|
|
}
|
|
|
|
|
|
// multiply band-diagonal matrix with vector
|
|
void mju_bandMulMatVec(mjtNum* res, const mjtNum* mat, const mjtNum* vec,
|
|
int ntotal, int nband, int ndense, int nvec, mjtByte flg_sym) {
|
|
int nsparse = ntotal-ndense;
|
|
|
|
// handle multiple vectors
|
|
for(int j=0; j < nvec; j++ ) {
|
|
// precompute pointer to corresponding vector in vec and res
|
|
const mjtNum* vec_j = vec + ntotal*j;
|
|
mjtNum* res_j = res + ntotal*j;
|
|
|
|
// sparse part
|
|
for(int i=0; i < nsparse; i++) {
|
|
int width = mjMIN(i+1, nband);
|
|
int adr = i*nband + nband - width;
|
|
int offset = mjMAX(0, i-nband+1);
|
|
res_j[i] = mju_dot(mat+adr, vec_j+offset, width); // lower triangle
|
|
if (flg_sym) {
|
|
// strict upper triangle
|
|
mju_addToScl(res_j+offset, mat+adr, vec_j[i], width-1);
|
|
}
|
|
}
|
|
|
|
// dense part
|
|
for(int i=nsparse; i < ntotal; i++) {
|
|
int adr = nsparse*nband + (i-nsparse)*ntotal;
|
|
res_j[i] = mju_dot(mat+adr, vec_j, i+1);
|
|
if (flg_sym) {
|
|
// strict upper triangle
|
|
mju_addToScl(res_j, mat+adr, vec_j[i], i);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
//------------------------------ LU factorization --------------------------------------------------
|
|
|
|
// sparse reverse-order LU factorization, no fill-in (assuming tree topology)
|
|
// result: LU = L + U; original = (U+I) * L; scratch size is n
|
|
void mju_factorLUSparse(mjtNum* LU, int n, int* scratch,
|
|
const int* rownnz, const int* rowadr, const int* colind) {
|
|
int* remaining = scratch;
|
|
|
|
// set remaining = rownnz
|
|
mju_copyInt(remaining, rownnz, n);
|
|
|
|
// diagonal elements (i,i)
|
|
for (int i=n-1; i >= 0; i--) {
|
|
// get address of last remaining element of row i, adjust remaining counter
|
|
int ii = rowadr[i] + remaining[i] - 1;
|
|
remaining[i]--;
|
|
|
|
// make sure ii is on diagonal
|
|
if (colind[ii] != i) {
|
|
mjERROR("missing diagonal element");
|
|
}
|
|
|
|
// make sure diagonal is not too small
|
|
if (mju_abs(LU[ii]) < mjMINVAL) {
|
|
mjERROR("diagonal element too small");
|
|
}
|
|
|
|
// rows j above i
|
|
for (int j=i-1; j >= 0; j--) {
|
|
// get address of last remaining element of row j
|
|
int ji = rowadr[j] + remaining[j] - 1;
|
|
|
|
// process row j if (j,i) is non-zero
|
|
if (colind[ji] == i) {
|
|
// adjust remaining counter
|
|
remaining[j]--;
|
|
|
|
// (j,i) = (j,i) / (i,i)
|
|
LU[ji] = LU[ji] / LU[ii];
|
|
mjtNum LUji = LU[ji];
|
|
|
|
// (j,k) = (j,k) - (i,k) * (j,i) for k<i; handle incompatible sparsity
|
|
int icnt = rowadr[i], jcnt = rowadr[j];
|
|
while (jcnt < rowadr[j]+remaining[j]) {
|
|
// both non-zero
|
|
if (colind[icnt] == colind[jcnt]) {
|
|
// update LU, advance counters
|
|
LU[jcnt++] -= LU[icnt++] * LUji;
|
|
}
|
|
|
|
// only (j,k) non-zero
|
|
else if (colind[icnt] > colind[jcnt]) {
|
|
// advance j counter
|
|
jcnt++;
|
|
}
|
|
|
|
// only (i,k) non-zero
|
|
else {
|
|
mjERROR("requires fill-in");
|
|
}
|
|
}
|
|
|
|
// make sure both rows fully processed
|
|
if (icnt != rowadr[i]+remaining[i] || jcnt != rowadr[j]+remaining[j]) {
|
|
mjERROR("row processing incomplete");
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// make sure remaining points to diagonal
|
|
for (int i=0; i < n; i++) {
|
|
if (remaining[i] < 0 || colind[rowadr[i]+remaining[i]] != i) {
|
|
mjERROR("unexpected sparse matrix structure");
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
// solve mat*res=vec given LU factorization of mat
|
|
void mju_solveLUSparse(mjtNum* res, const mjtNum* LU, const mjtNum* vec, int n,
|
|
const int* rownnz, const int* rowadr, const int* diag, const int* colind) {
|
|
// solve (U+I)*res = vec
|
|
for (int i=n-1; i >= 0; i--) {
|
|
// init: diagonal of (U+I) is 1
|
|
res[i] = vec[i];
|
|
|
|
int d1 = diag[i]+1;
|
|
int nnz = rownnz[i] - d1;
|
|
if (nnz > 0) {
|
|
int adr = rowadr[i] + d1;
|
|
res[i] -= mju_dotSparse(LU+adr, res, nnz, colind+adr);
|
|
}
|
|
}
|
|
|
|
//------------------ solve L*res(new) = res
|
|
for (int i=0; i < n; i++) {
|
|
// res[i] -= sum_k<i res[k]*LU(i,k)
|
|
int d = diag[i];
|
|
int adr = rowadr[i];
|
|
if (d > 0) {
|
|
res[i] -= mju_dotSparse(LU+adr, res, d, colind+adr);
|
|
}
|
|
|
|
// divide by diagonal element of L
|
|
res[i] /= LU[adr + d];
|
|
}
|
|
}
|
|
|
|
|
|
//--------------------------- eigen decomposition --------------------------------------------------
|
|
|
|
// eigenvalue decomposition of symmetric 3x3 matrix
|
|
static const mjtNum eigEPS = mjMINVAL * 1000;
|
|
int mju_eig3(mjtNum eigval[3], mjtNum eigvec[9], mjtNum quat[4], const mjtNum mat[9]) {
|
|
mjtNum D[9], tmp[9];
|
|
mjtNum tau, t, c;
|
|
int iter, rk, ck, rotk;
|
|
|
|
// initialize with unit quaternion
|
|
quat[0] = 1;
|
|
quat[1] = quat[2] = quat[3] = 0;
|
|
|
|
// Jacobi iteration
|
|
for (iter=0; iter < 500; iter++) {
|
|
// make quaternion matrix eigvec, compute D = eigvec'*mat*eigvec
|
|
mju_quat2Mat(eigvec, quat);
|
|
mju_mulMatTMat3(tmp, eigvec, mat);
|
|
mju_mulMatMat3(D, tmp, eigvec);
|
|
|
|
// assign eigenvalues
|
|
eigval[0] = D[0];
|
|
eigval[1] = D[4];
|
|
eigval[2] = D[8];
|
|
|
|
// find max off-diagonal element, set indices
|
|
if (mju_abs(D[1]) > mju_abs(D[2]) && mju_abs(D[1]) > mju_abs(D[5])) {
|
|
rk = 0; // row
|
|
ck = 1; // column
|
|
rotk = 2; // rotation axis
|
|
} else if (mju_abs(D[2]) > mju_abs(D[5])) {
|
|
rk = 0;
|
|
ck = 2;
|
|
rotk = 1;
|
|
} else {
|
|
rk = 1;
|
|
ck = 2;
|
|
rotk = 0;
|
|
}
|
|
|
|
// terminate if max off-diagonal element too small
|
|
if (mju_abs(D[3*rk+ck]) < eigEPS) {
|
|
break;
|
|
}
|
|
|
|
// 2x2 symmetric Schur decomposition
|
|
tau = (D[4*ck]-D[4*rk])/(2*D[3*rk+ck]);
|
|
if (tau >= 0) {
|
|
t = 1.0/(tau + mju_sqrt(1 + tau*tau));
|
|
} else {
|
|
t = -1.0/(-tau + mju_sqrt(1 + tau*tau));
|
|
}
|
|
c = 1.0/mju_sqrt(1 + t*t);
|
|
|
|
// terminate if cosine too close to 1
|
|
if (c > 1.0-eigEPS) {
|
|
break;
|
|
}
|
|
|
|
// express rotation as quaternion
|
|
tmp[1] = tmp[2] = tmp[3] = 0;
|
|
tmp[rotk+1] = (tau >= 0 ? -mju_sqrt(0.5-0.5*c) : mju_sqrt(0.5-0.5*c));
|
|
if (rotk == 1) {
|
|
tmp[rotk+1] = -tmp[rotk+1];
|
|
}
|
|
tmp[0] = mju_sqrt(1.0 - tmp[rotk+1]*tmp[rotk+1]);
|
|
mju_normalize4(tmp);
|
|
|
|
// accumulate quaternion rotation
|
|
mju_mulQuat(quat, quat, tmp);
|
|
mju_normalize4(quat);
|
|
}
|
|
|
|
// sort eigenvalues in decreasing order (bubblesort: 0, 1, 0)
|
|
for (int j=0; j < 3; j++) {
|
|
int j1 = j%2; // lead index
|
|
|
|
// only swap if the eigenvalues are different
|
|
if (eigval[j1]+eigEPS < eigval[j1+1]) {
|
|
// swap eigenvalues
|
|
t = eigval[j1];
|
|
eigval[j1] = eigval[j1+1];
|
|
eigval[j1+1] = t;
|
|
|
|
// rotate quaternion
|
|
tmp[0] = 0.707106781186548; // = cos(pi/4) = sin(pi/4)
|
|
tmp[1] = tmp[2] = tmp[3] = 0;
|
|
tmp[(j1+2)%3+1] = tmp[0];
|
|
mju_mulQuat(quat, quat, tmp);
|
|
mju_normalize4(quat);
|
|
}
|
|
}
|
|
|
|
// recompute eigvec
|
|
mju_quat2Mat(eigvec, quat);
|
|
|
|
return iter;
|
|
}
|
|
|
|
|
|
//---------------------------------- QCQP ----------------------------------------------------------
|
|
|
|
// solve QCQP in 2 dimensions:
|
|
// min 0.5*x'*A*x + x'*b s.t. sum (xi/di)^2 <= r^2
|
|
// return 0 if unconstrained, 1 if constrained
|
|
int mju_QCQP2(mjtNum* res, const mjtNum* Ain, const mjtNum* bin,
|
|
const mjtNum* d, mjtNum r) {
|
|
mjtNum A11, A22, A12, b1, b2;
|
|
mjtNum P11, P22, P12, det, detinv, v1, v2, la, val, deriv;
|
|
|
|
// scale A,b so that constraint becomes x'*x <= r*r
|
|
b1 = bin[0]*d[0];
|
|
b2 = bin[1]*d[1];
|
|
A11 = Ain[0]*d[0]*d[0];
|
|
A22 = Ain[3]*d[1]*d[1];
|
|
A12 = Ain[1]*d[0]*d[1];
|
|
|
|
// Newton iteration
|
|
la = 0;
|
|
for (int iter=0; iter < 20; iter++) {
|
|
// det(A+la)
|
|
det = (A11+la)*(A22+la) - A12*A12;
|
|
|
|
// check SPD, with 1e-10 threshold
|
|
if (det < 1e-10) {
|
|
res[0] = 0;
|
|
res[1] = 0;
|
|
return 0;
|
|
}
|
|
|
|
// P = inv(A+la)
|
|
detinv = 1/det;
|
|
P11 = (A22+la)*detinv;
|
|
P22 = (A11+la)*detinv;
|
|
P12 = -A12*detinv;
|
|
|
|
// v = -P*b
|
|
v1 = -P11*b1 - P12*b2;
|
|
v2 = -P12*b1 - P22*b2;
|
|
|
|
// val = v'*v - r*r
|
|
val = v1*v1 + v2*v2 - r*r;
|
|
|
|
// check for convergence, or initial solution inside constraint set
|
|
if (val < 1e-10) {
|
|
break;
|
|
}
|
|
|
|
// deriv = -2 * v' * P * v
|
|
deriv = -2.0*(P11*v1*v1 + 2.0*P12*v1*v2 + P22*v2*v2);
|
|
|
|
// compute update, exit if too small
|
|
mjtNum delta = -val/deriv;
|
|
if (delta < 1e-10) {
|
|
break;
|
|
}
|
|
|
|
// update
|
|
la += delta;
|
|
}
|
|
|
|
// undo scaling
|
|
res[0] = v1*d[0];
|
|
res[1] = v2*d[1];
|
|
|
|
return (la != 0);
|
|
}
|
|
|
|
|
|
// solve QCQP in 3 dimensions:
|
|
// min 0.5*x'*A*x + x'*b s.t. sum (xi/di)^2 <= r^2
|
|
// return 0 if unconstrained, 1 if constrained
|
|
int mju_QCQP3(mjtNum* res, const mjtNum* Ain, const mjtNum* bin,
|
|
const mjtNum* d, mjtNum r) {
|
|
mjtNum A11, A22, A33, A12, A13, A23, b1, b2, b3;
|
|
mjtNum P11, P22, P33, P12, P13, P23, det, detinv, v1, v2, v3, la, val, deriv;
|
|
|
|
// scale A,b so that constraint becomes x'*x <= r*r
|
|
b1 = bin[0]*d[0];
|
|
b2 = bin[1]*d[1];
|
|
b3 = bin[2]*d[2];
|
|
A11 = Ain[0]*d[0]*d[0];
|
|
A22 = Ain[4]*d[1]*d[1];
|
|
A33 = Ain[8]*d[2]*d[2];
|
|
A12 = Ain[1]*d[0]*d[1];
|
|
A13 = Ain[2]*d[0]*d[2];
|
|
A23 = Ain[5]*d[1]*d[2];
|
|
|
|
// Newton iteration
|
|
la = 0;
|
|
for (int iter=0; iter < 20; iter++) {
|
|
// unscaled P
|
|
P11 = (A22+la)*(A33+la) - A23*A23;
|
|
P22 = (A11+la)*(A33+la) - A13*A13;
|
|
P33 = (A11+la)*(A22+la) - A12*A12;
|
|
P12 = A13*A23 - A12*(A33+la);
|
|
P13 = A12*A23 - A13*(A22+la);
|
|
P23 = A12*A13 - A23*(A11+la);
|
|
|
|
// det(A+la)
|
|
det = (A11+la)*P11 + A12*P12 + A13*P13;
|
|
|
|
// check SPD, with 1e-10 threshold
|
|
if (det < 1e-10) {
|
|
res[0] = 0;
|
|
res[1] = 0;
|
|
res[2] = 0;
|
|
return 0;
|
|
}
|
|
|
|
// detinv
|
|
detinv = 1/det;
|
|
|
|
// final P
|
|
P11 *= detinv;
|
|
P22 *= detinv;
|
|
P33 *= detinv;
|
|
P12 *= detinv;
|
|
P13 *= detinv;
|
|
P23 *= detinv;
|
|
|
|
// v = -P*b
|
|
v1 = -P11*b1 - P12*b2 - P13*b3;
|
|
v2 = -P12*b1 - P22*b2 - P23*b3;
|
|
v3 = -P13*b1 - P23*b2 - P33*b3;
|
|
|
|
// val = v'*v - r*r
|
|
val = v1*v1 + v2*v2 + v3*v3 - r*r;
|
|
|
|
// check for convergence, or initial solution inside constraint set
|
|
if (val < 1e-10) {
|
|
break;
|
|
}
|
|
|
|
// deriv = -2 * v' * P * v
|
|
deriv = -2.0*(P11*v1*v1 + P22*v2*v2 + P33*v3*v3)
|
|
-4.0*(P12*v1*v2 + P13*v1*v3 + P23*v2*v3);
|
|
|
|
// compute update, exit if too small
|
|
mjtNum delta = -val/deriv;
|
|
if (delta < 1e-10) {
|
|
break;
|
|
}
|
|
|
|
// update
|
|
la += delta;
|
|
}
|
|
|
|
// undo scaling
|
|
res[0] = v1*d[0];
|
|
res[1] = v2*d[1];
|
|
res[2] = v3*d[2];
|
|
|
|
return (la != 0);
|
|
}
|
|
|
|
|
|
// solve QCQP in n dimensions:
|
|
// min 0.5*x'*A*x + x'*b s.t. sum (xi/di)^2 <= r^2
|
|
// return 0 if unconstrained, 1 if constrained
|
|
int mju_QCQP(mjtNum* res, const mjtNum* Ain, const mjtNum* bin,
|
|
const mjtNum* d, mjtNum r, int n) {
|
|
mjtNum A[25], Ala[25], b[5];
|
|
mjtNum la, val, deriv, tmp[5];
|
|
|
|
// check size
|
|
if (n > 5) {
|
|
mjERROR("n is only supported up to 5");
|
|
}
|
|
|
|
// scale A,b so that constraint becomes x'*x <= r*r
|
|
for (int i=0; i < n; i++) {
|
|
b[i] = bin[i] * d[i];
|
|
|
|
for (int j=0; j < n; j++) {
|
|
A[j+i*n] = Ain[j+i*n] * d[i] * d[j];
|
|
}
|
|
}
|
|
|
|
// Newton iteration
|
|
la = 0;
|
|
for (int iter=0; iter < 20; iter++) {
|
|
// make A+la
|
|
mju_copy(Ala, A, n*n);
|
|
for (int i=0; i < n; i++) {
|
|
Ala[i*(n+1)] += la;
|
|
}
|
|
|
|
// factorize, check rank with 1e-10 threshold
|
|
if (mju_cholFactor(Ala, n, 1e-10) < n) {
|
|
mju_zero(res, n);
|
|
return 0;
|
|
}
|
|
|
|
// set res = -Ala \ b
|
|
mju_cholSolve(res, Ala, b, n);
|
|
mju_scl(res, res, -1, n);
|
|
|
|
// val = b' * Ala^-2 * b - r*r
|
|
val = mju_dot(res, res, n) - r*r;
|
|
|
|
// check for convergence, or initial solution inside constraint set
|
|
if (val < 1e-10) {
|
|
break;
|
|
}
|
|
|
|
// deriv = -2 * b' * Ala^-3 * b
|
|
mju_cholSolve(tmp, Ala, res, n);
|
|
deriv = -2.0 * mju_dot(res, tmp, n);
|
|
|
|
// compute update, exit if too small
|
|
mjtNum delta = -val/deriv;
|
|
if (delta < 1e-10) {
|
|
break;
|
|
}
|
|
|
|
// update
|
|
la += delta;
|
|
}
|
|
|
|
// undo scaling
|
|
for (int i=0; i < n; i++) {
|
|
res[i] = res[i] * d[i];
|
|
}
|
|
|
|
return (la != 0);
|
|
}
|
|
|
|
|
|
//--------------------------- box-constrained quadratic program ------------------------------------
|
|
|
|
// minimize 0.5*x'*H*x + x'*g s.t. lower <= x <= upper, return rank or -1 if failed
|
|
// inputs:
|
|
// n - problem dimension
|
|
// H - SPD matrix n*n
|
|
// g - bias vector n
|
|
// lower - lower bounds n
|
|
// upper - upper bounds n
|
|
// res - solution warmstart n
|
|
// return value:
|
|
// nfree <= n - rank of unconstrained subspace, -1 if failure
|
|
// outputs (required):
|
|
// res - solution n
|
|
// R - subspace Cholesky factor nfree*nfree allocated: n*(n+7)
|
|
// outputs (optional):
|
|
// index - set of free dimensions nfree allocated: n
|
|
// notes:
|
|
// the initial value of res is used to warmstart the solver
|
|
// R must have allocatd size n*(n+7), but only nfree*nfree values are used in output
|
|
// index (if given) must have allocated size n, but only nfree values are used in output
|
|
// only lower triangles of H and R and read from and written to, respectively
|
|
int mju_boxQP(mjtNum* res, mjtNum* R, int* index,
|
|
const mjtNum* H, const mjtNum* g, int n,
|
|
const mjtNum* lower, const mjtNum* upper) {
|
|
// algorithm options
|
|
int maxiter = 100; // maximum number of iterations
|
|
mjtNum mingrad = 1E-16; // minimum squared norm of (unclamped) gradient
|
|
mjtNum backtrack = 0.5; // backtrack factor for decreasing stepsize
|
|
mjtNum minstep = 1E-22; // minimum stepsize for linesearch
|
|
mjtNum armijo = 0.1; // Armijo parameter (fraction of expected linear improvement)
|
|
|
|
// logging (disabled)
|
|
char* log = NULL; // buffer to write log messages into
|
|
int logsz = 0; // size of log buffer
|
|
|
|
return mju_boxQPoption(res, R, index, H, g, n, lower, upper,
|
|
maxiter, mingrad, backtrack, minstep, armijo, log, logsz);
|
|
}
|
|
|
|
|
|
// allocate heap memory for box-constrained Quadratic Program
|
|
// as in mju_boxQP, index, lower and upper are optional
|
|
// free all pointers with mju_free()
|
|
void mju_boxQPmalloc(mjtNum** res, mjtNum** R, int** index,
|
|
mjtNum** H, mjtNum** g, int n,
|
|
mjtNum** lower, mjtNum** upper) {
|
|
// required arrays
|
|
*res = (mjtNum*) mju_malloc(sizeof(mjtNum)*n);
|
|
*R = (mjtNum*) mju_malloc(sizeof(mjtNum)*n*(n+7));
|
|
*H = (mjtNum*) mju_malloc(sizeof(mjtNum)*n*n);
|
|
*g = (mjtNum*) mju_malloc(sizeof(mjtNum)*n);
|
|
|
|
// optional arrays
|
|
if (lower) *lower = (mjtNum*) mju_malloc(sizeof(mjtNum)*n);
|
|
if (upper) *upper = (mjtNum*) mju_malloc(sizeof(mjtNum)*n);
|
|
if (index) *index = (int*) mju_malloc(sizeof(int)*n);
|
|
}
|
|
|
|
|
|
// local enum encoding mju_boxQP solver status (purely for readability)
|
|
enum mjtStatusBoxQP {
|
|
mjBOXQP_NOT_SPD = -1, // Hessian is not positive definite
|
|
mjBOXQP_NO_DESCENT = 0, // no descent direction found
|
|
mjBOXQP_MAX_ITER = 1, // maximum main iterations exceeded
|
|
mjBOXQP_MAX_LS_ITER = 2, // maximum line-search iterations exceeded
|
|
mjBOXQP_TOL_GRAD = 3, // gradient norm smaller than tolerance
|
|
mjBOXQP_UNBOUNDED = 4, // no dimensions clamped, returning Newton point
|
|
mjBOXQP_ALL_CLAMPED = 5, // all dimensions clamped
|
|
|
|
mjNBOXQP = 7 // number of boxQP status values
|
|
};
|
|
|
|
|
|
// multiply symmetric matrix with vector on both sides: return vec'*mat*vec
|
|
// assumes symmetry of mat, ignores upper triangle
|
|
static mjtNum mulVecMatVecSym(const mjtNum* vec, const mjtNum* mat, int n) {
|
|
mjtNum res = 0;
|
|
for (int i=0; i < n; i++) {
|
|
res += vec[i] * mat[n*i+i] * vec[i]; // diagonal
|
|
res += 2 * vec[i] * mju_dot(mat+n*i, vec, i); // off-diagonal
|
|
}
|
|
return res;
|
|
}
|
|
|
|
|
|
// minimize 0.5*x'*H*x + x'*g s.t. lower <= x <=upper, explicit options
|
|
// additional arguments to mju_boxQP (see mju_boxQP documentation):
|
|
// maxiter maximum number of iterations
|
|
// mingrad minimum squared norm of (unclamped) gradient
|
|
// backtrack backtrack factor for decreasing stepsize
|
|
// minstep minimum stepsize for linesearch
|
|
// armijo Armijo parameter (fraction of expected linear improvement)
|
|
// log buffer to write log messages into
|
|
// logsz size of log buffer
|
|
int mju_boxQPoption(mjtNum* res, mjtNum* R, int* index, // outputs
|
|
const mjtNum* H, const mjtNum* g, int n, // QP definition
|
|
const mjtNum* lower, const mjtNum* upper, // bounds
|
|
int maxiter, mjtNum mingrad, mjtNum backtrack, // options
|
|
mjtNum minstep, mjtNum armijo, // options
|
|
char* log, int logsz) // logging
|
|
{
|
|
int status = mjBOXQP_NO_DESCENT; // initial status: no descent direction found
|
|
int factorize = 1; // always factorize on the first iteration
|
|
int nfree = n; // initialise nfree with n
|
|
int nfactor = 0;
|
|
mjtNum sdotg, improvement=0, value=0, norm2=0;
|
|
|
|
// basic checks
|
|
if (n <= 0) {
|
|
mjERROR("problem size n must be positive");
|
|
}
|
|
if (upper && lower) {
|
|
for (int i=0; i < n; i++) {
|
|
if (lower[i] >= upper[i]) {
|
|
mjERROR("upper bounds must be stricly larger than lower bounds");
|
|
}
|
|
}
|
|
}
|
|
|
|
// local scratch vectors, allocate in R
|
|
mjtNum* scratch = R + n*n;
|
|
mjtNum* grad = scratch + 0*n;
|
|
mjtNum* search = scratch + 1*n;
|
|
mjtNum* candidate = scratch + 2*n;
|
|
mjtNum* temp = scratch + 3*n;
|
|
int* clamped = (int*) (scratch + 4*n);
|
|
int* oldclamped = (int*) (scratch + 5*n);
|
|
|
|
// if index vector not given, use scratch space
|
|
if (!index) {
|
|
index = (int*) (scratch + 6*n);
|
|
}
|
|
|
|
static const char status_string[mjNBOXQP][50]= {
|
|
"Hessian is not positive definite",
|
|
"No descent direction found",
|
|
"Maximum main iterations exceeded",
|
|
"Maximum line-search iterations exceeded",
|
|
"Gradient norm smaller than tolerance",
|
|
"No dimensions clamped, returning Newton point",
|
|
"All dimensions clamped"
|
|
};
|
|
|
|
// no bounds: return Newton point
|
|
if (!lower && !upper) {
|
|
// try to factorize
|
|
mju_copy(R, H, n*n);
|
|
int rank = mju_cholFactor(R, n, mjMINVAL);
|
|
if (rank == n) {
|
|
mju_cholSolve(res, R, g, n);
|
|
mju_scl(res, res, -1, n);
|
|
nfactor = 1;
|
|
status = mjBOXQP_UNBOUNDED;
|
|
} else {
|
|
status = mjBOXQP_NOT_SPD;
|
|
}
|
|
|
|
// full index set (no clamping)
|
|
for (int i=0; i < n; i++) {
|
|
index[i] = i;
|
|
}
|
|
}
|
|
|
|
// have bounds: clamp res
|
|
else {
|
|
for (int i=0; i < n; i++) {
|
|
if (lower) {
|
|
res[i] = mju_max(res[i], lower[i]);
|
|
}
|
|
if (upper) {
|
|
res[i] = mju_min(res[i], upper[i]);
|
|
}
|
|
}
|
|
}
|
|
|
|
// ------ main loop
|
|
int iter, logptr = 0;
|
|
mjtNum oldvalue;
|
|
for (iter=0; iter < maxiter; iter++) {
|
|
if (status != mjBOXQP_NO_DESCENT) {
|
|
break;
|
|
}
|
|
|
|
// compute objective: value = 0.5*res'*H*res + res'*g
|
|
value = 0.5 * mulVecMatVecSym(res, H, n) + mju_dot(res, g, n);
|
|
|
|
// save last value
|
|
oldvalue = value;
|
|
|
|
// compute gradient
|
|
mju_mulMatVec(grad, H, res, n, n);
|
|
mju_addTo(grad, g, n);
|
|
|
|
// find clamped dimensions
|
|
for (int i=0; i < n; i++) {
|
|
clamped[i] = ( lower && res[i] == lower[i] && grad[i] > 0 ) ||
|
|
( upper && res[i] == upper[i] && grad[i] < 0 );
|
|
}
|
|
|
|
// build index of free dimensions, count them
|
|
nfree = 0;
|
|
for (int i=0; i < n; i++) {
|
|
if (!clamped[i]) {
|
|
index[nfree++] = i;
|
|
}
|
|
}
|
|
|
|
// all dimensions are clamped: minimum found
|
|
if (!nfree) {
|
|
status = mjBOXQP_ALL_CLAMPED;
|
|
break;
|
|
}
|
|
|
|
// re-factorize if clamped dimensions have changed
|
|
if (iter) {
|
|
factorize = 0;
|
|
for (int i=0; i < n; i++) {
|
|
if (clamped[i] != oldclamped[i]) {
|
|
factorize = 1;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
// save last clamped
|
|
for (int i=0; i < n; i++) {
|
|
oldclamped[i] = clamped[i];
|
|
}
|
|
|
|
// get search direction: search = g + H_all,clamped * res_clamped
|
|
for (int i=0; i < n; i++) {
|
|
temp[i] = clamped[i] ? res[i] : 0;
|
|
}
|
|
mju_mulMatVec(search, H, temp, n, n);
|
|
mju_addTo(search, g, n);
|
|
|
|
// search = compress_free(search)
|
|
for (int i=0; i < nfree; i++) {
|
|
search[i] = search[index[i]];
|
|
}
|
|
|
|
// R = compress_free(H)
|
|
if (factorize) {
|
|
for (int i=0; i < nfree; i++) {
|
|
for (int j=0; j < i+1; j++) {
|
|
R[i*nfree+j] = H[index[i]*n+index[j]];
|
|
}
|
|
}
|
|
}
|
|
|
|
// re-factorize and increment counter, if required
|
|
int rank = factorize ? mju_cholFactor(R, nfree, mjMINVAL) : nfree;
|
|
nfactor += factorize;
|
|
|
|
// abort if factorization failed
|
|
if (rank != nfree) {
|
|
status = mjBOXQP_NOT_SPD;
|
|
break;
|
|
}
|
|
|
|
// temp = H_free,free \ search_free
|
|
mju_cholSolve(temp, R, search, nfree);
|
|
|
|
// search_free = expand_free(-temp) - x_free
|
|
mju_zero(search, n);
|
|
for (int i=0; i < nfree; i++) {
|
|
search[index[i]] = -temp[i] -res[index[i]];
|
|
}
|
|
|
|
// ------ check gradient
|
|
|
|
// squared norm of free gradient
|
|
norm2 = 0;
|
|
for (int i=0; i < nfree; i++) {
|
|
mjtNum grad_i = grad[index[i]];
|
|
norm2 += grad_i*grad_i;
|
|
}
|
|
|
|
// small gradient: minimum found
|
|
if (norm2 < mingrad) {
|
|
status = nfree == n ? mjBOXQP_UNBOUNDED : mjBOXQP_TOL_GRAD;
|
|
break;
|
|
}
|
|
|
|
// sanity check: make sure we have a descent direction
|
|
if ((sdotg = mju_dot(search, grad, n)) >= 0) {
|
|
break; // SHOULD NOT OCCUR
|
|
}
|
|
|
|
// ------ projected Armijo line search
|
|
mjtNum step = 1;
|
|
int nstep = 0;
|
|
do {
|
|
// candidate = clamp(x + step*search)
|
|
mju_scl(candidate, search, step, n);
|
|
mju_addTo(candidate, res, n);
|
|
for (int i=0; i < n; i++) {
|
|
if (lower && candidate[i] < lower[i]) {
|
|
candidate[i] = lower[i];
|
|
} else if (upper && candidate[i] > upper[i]) {
|
|
candidate[i] = upper[i];
|
|
}
|
|
}
|
|
|
|
// new objective value
|
|
value = 0.5 * mulVecMatVecSym(candidate, H, n) + mju_dot(candidate, g, n);
|
|
|
|
// increment and break if step is too small
|
|
nstep++;
|
|
step = step*backtrack;
|
|
if (step < minstep) {
|
|
status = mjBOXQP_MAX_LS_ITER;
|
|
break;
|
|
}
|
|
|
|
// repeat until relative improvement >= Armijo
|
|
improvement = (value - oldvalue) / (step*sdotg);
|
|
} while (improvement < armijo);
|
|
|
|
|
|
// print iteration info
|
|
if (log) {
|
|
logptr += snprintf(log+logptr, logsz-logptr,
|
|
"iter %-3d: |grad|: %-8.2g reduction: %-8.2g improvement: %-8.4g "
|
|
"linesearch: %g^%-2d factorized: %d nfree: %d\n",
|
|
iter+1, mju_sqrt(norm2), oldvalue-value, improvement,
|
|
backtrack, nstep-1, factorize, nfree);
|
|
}
|
|
|
|
// accept candidate
|
|
mju_copy(res, candidate, n);
|
|
}
|
|
|
|
// max iterations exceeded
|
|
if (iter == maxiter) {
|
|
status = mjBOXQP_MAX_ITER;
|
|
}
|
|
|
|
// print final info
|
|
if (log) {
|
|
snprintf(log+logptr, logsz-logptr, "BOXQP: %s.\n"
|
|
"iterations= %d, factorizations= %d, |grad|= %-12.6g, final value= %-12.6g\n",
|
|
status_string[status+1], iter, nfactor, mju_sqrt(norm2), value);
|
|
}
|
|
|
|
// return nf or -1 if failure
|
|
return (status == mjBOXQP_NO_DESCENT || status == mjBOXQP_NOT_SPD) ? -1 : nfree;
|
|
}
|