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diff --git a/patches/ccpi-regularisation-toolkit-fast-tnv/TNV_core.c b/patches/ccpi-regularisation-toolkit-fast-tnv/TNV_core.c
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+++ b/patches/ccpi-regularisation-toolkit-fast-tnv/TNV_core.c
@@ -0,0 +1,668 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <malloc.h>
+#include "TNV_core.h"
+
+#define BLOCK 32
+#define min(a,b) (((a)<(b))?(a):(b))
+
+inline void coefF(float *t, float M1, float M2, float M3, float sigma, int p, int q, int r) {
+ int ii, num;
+ float divsigma = 1.0f / sigma;
+ float sum, shrinkfactor;
+ float T,D,det,eig1,eig2,sig1,sig2,V1, V2, V3, V4, v0,v1,v2, mu1,mu2,sig1_upd,sig2_upd;
+ float proj[2] = {0};
+
+ // Compute eigenvalues of M
+ T = M1 + M3;
+ D = M1 * M3 - M2 * M2;
+ det = sqrtf(MAX((T * T / 4.0f) - D, 0.0f));
+ eig1 = MAX((T / 2.0f) + det, 0.0f);
+ eig2 = MAX((T / 2.0f) - det, 0.0f);
+ sig1 = sqrtf(eig1);
+ sig2 = sqrtf(eig2);
+
+ // Compute normalized eigenvectors
+ V1 = V2 = V3 = V4 = 0.0f;
+
+ if(M2 != 0.0f)
+ {
+ v0 = M2;
+ v1 = eig1 - M3;
+ v2 = eig2 - M3;
+
+ mu1 = sqrtf(v0 * v0 + v1 * v1);
+ mu2 = sqrtf(v0 * v0 + v2 * v2);
+
+ if(mu1 > fTiny)
+ {
+ V1 = v1 / mu1;
+ V3 = v0 / mu1;
+ }
+
+ if(mu2 > fTiny)
+ {
+ V2 = v2 / mu2;
+ V4 = v0 / mu2;
+ }
+
+ } else
+ {
+ if(M1 > M3)
+ {
+ V1 = V4 = 1.0f;
+ V2 = V3 = 0.0f;
+
+ } else
+ {
+ V1 = V4 = 0.0f;
+ V2 = V3 = 1.0f;
+ }
+ }
+
+ // Compute prox_p of the diagonal entries
+ sig1_upd = sig2_upd = 0.0f;
+
+ if(p == 1)
+ {
+ sig1_upd = MAX(sig1 - divsigma, 0.0f);
+ sig2_upd = MAX(sig2 - divsigma, 0.0f);
+
+ } else if(p == INFNORM)
+ {
+ proj[0] = sigma * fabs(sig1);
+ proj[1] = sigma * fabs(sig2);
+
+ /*l1 projection part */
+ sum = fLarge;
+ num = 0l;
+ shrinkfactor = 0.0f;
+ while(sum > 1.0f)
+ {
+ sum = 0.0f;
+ num = 0;
+
+ for(ii = 0; ii < 2; ii++)
+ {
+ proj[ii] = MAX(proj[ii] - shrinkfactor, 0.0f);
+
+ sum += fabs(proj[ii]);
+ if(proj[ii]!= 0.0f)
+ num++;
+ }
+
+ if(num > 0)
+ shrinkfactor = (sum - 1.0f) / num;
+ else
+ break;
+ }
+ /*l1 proj ends*/
+
+ sig1_upd = sig1 - divsigma * proj[0];
+ sig2_upd = sig2 - divsigma * proj[1];
+ }
+
+ // Compute the diagonal entries of $\widehat{\Sigma}\Sigma^{\dagger}_0$
+ if(sig1 > fTiny)
+ sig1_upd /= sig1;
+
+ if(sig2 > fTiny)
+ sig2_upd /= sig2;
+
+ // Compute solution
+ t[0] = sig1_upd * V1 * V1 + sig2_upd * V2 * V2;
+ t[1] = sig1_upd * V1 * V3 + sig2_upd * V2 * V4;
+ t[2] = sig1_upd * V3 * V3 + sig2_upd * V4 * V4;
+}
+
+
+#include "hw_sched.h"
+typedef _Float16 floatxx; // Large arrays, allways float16 if we go mixed-precision.
+//typedef _Float16 floatyy; // Small arrays which we can do both ways.
+typedef float floatyy;
+
+typedef struct {
+ int offY, stepY, copY;
+ floatxx *Input, *u, *qx, *qy, *gradx, *grady, *div;
+ floatyy *div0, *udiff0;
+ floatyy *gradxdiff, *gradydiff, *ubarx, *ubary, *udiff;
+ float resprimal, resdual;
+ float unorm, qnorm, product;
+} tnv_thread_t;
+
+typedef struct {
+ int threads;
+ tnv_thread_t *thr_ctx;
+ float *InputT, *uT;
+ int dimX, dimY, dimZ, padZ;
+ float lambda, sigma, tau, theta;
+} tnv_context_t;
+
+HWSched sched = NULL;
+tnv_context_t tnv_ctx;
+
+
+static int tnv_free(HWThread thr, void *hwctx, int device_id, void *data) {
+ int i,j,k;
+ tnv_context_t *tnv_ctx = (tnv_context_t*)data;
+ tnv_thread_t *ctx = tnv_ctx->thr_ctx + device_id;
+
+ free(ctx->Input);
+ free(ctx->u);
+ free(ctx->qx);
+ free(ctx->qy);
+ free(ctx->gradx);
+ free(ctx->grady);
+ free(ctx->div);
+
+ free(ctx->div0);
+ free(ctx->udiff0);
+
+ free(ctx->gradxdiff);
+ free(ctx->gradydiff);
+ free(ctx->ubarx);
+ free(ctx->ubary);
+ free(ctx->udiff);
+
+ return 0;
+}
+
+static int tnv_init(HWThread thr, void *hwctx, int device_id, void *data) {
+ tnv_context_t *tnv_ctx = (tnv_context_t*)data;
+ tnv_thread_t *ctx = tnv_ctx->thr_ctx + device_id;
+
+ int dimX = tnv_ctx->dimX;
+ int dimY = tnv_ctx->dimY;
+ int dimZ = tnv_ctx->dimZ;
+ int padZ = tnv_ctx->padZ;
+ int offY = ctx->offY;
+ int stepY = ctx->stepY;
+
+// printf("%i %p - %i %i %i x %i %i\n", device_id, ctx, dimX, dimY, dimZ, offY, stepY);
+
+ long DimTotal = (long)(dimX*stepY*padZ);
+ long Dim1Total = (long)(dimX*(stepY+1)*padZ);
+ long DimRow = (long)(dimX * padZ);
+ long DimCell = (long)(padZ);
+
+ // Auxiliar vectors
+ ctx->Input = memalign(64, Dim1Total * sizeof(floatxx));
+ ctx->u = memalign(64, Dim1Total * sizeof(floatxx));
+ ctx->qx = memalign(64, DimTotal * sizeof(floatxx));
+ ctx->qy = memalign(64, DimTotal * sizeof(floatxx));
+ ctx->gradx = memalign(64, DimTotal * sizeof(floatxx));
+ ctx->grady = memalign(64, DimTotal * sizeof(floatxx));
+ ctx->div = memalign(64, Dim1Total * sizeof(floatxx));
+
+ ctx->div0 = memalign(64, DimRow * sizeof(floatyy));
+ ctx->udiff0 = memalign(64, DimRow * sizeof(floatyy));
+
+ ctx->gradxdiff = memalign(64, DimCell * sizeof(floatyy));
+ ctx->gradydiff = memalign(64, DimCell * sizeof(floatyy));
+ ctx->ubarx = memalign(64, DimCell * sizeof(floatyy));
+ ctx->ubary = memalign(64, DimCell * sizeof(floatyy));
+ ctx->udiff = memalign(64, DimCell * sizeof(floatyy));
+
+ if ((!ctx->Input)||(!ctx->u)||(!ctx->qx)||(!ctx->qy)||(!ctx->gradx)||(!ctx->grady)||(!ctx->div)||(!ctx->div0)||(!ctx->udiff)||(!ctx->udiff0)) {
+ fprintf(stderr, "Error allocating memory\n");
+ exit(-1);
+ }
+
+ return 0;
+}
+
+static int tnv_start(HWThread thr, void *hwctx, int device_id, void *data) {
+ int i,j,k;
+ tnv_context_t *tnv_ctx = (tnv_context_t*)data;
+ tnv_thread_t *ctx = tnv_ctx->thr_ctx + device_id;
+
+ int dimX = tnv_ctx->dimX;
+ int dimY = tnv_ctx->dimY;
+ int dimZ = tnv_ctx->dimZ;
+ int padZ = tnv_ctx->padZ;
+ int offY = ctx->offY;
+ int stepY = ctx->stepY;
+ int copY = ctx->copY;
+
+// printf("%i %p - %i %i %i (%i) x %i %i\n", device_id, ctx, dimX, dimY, dimZ, padZ, offY, stepY);
+
+ long DimTotal = (long)(dimX*stepY*padZ);
+ long Dim1Total = (long)(dimX*copY*padZ);
+
+ memset(ctx->u, 0, Dim1Total * sizeof(floatxx));
+ memset(ctx->qx, 0, DimTotal * sizeof(floatxx));
+ memset(ctx->qy, 0, DimTotal * sizeof(floatxx));
+ memset(ctx->gradx, 0, DimTotal * sizeof(floatxx));
+ memset(ctx->grady, 0, DimTotal * sizeof(floatxx));
+ memset(ctx->div, 0, Dim1Total * sizeof(floatxx));
+
+ for(k=0; k<dimZ; k++) {
+ for(j=0; j<copY; j++) {
+ for(i=0; i<dimX; i++) {
+ ctx->Input[j * dimX * padZ + i * padZ + k] = tnv_ctx->InputT[k * dimX * dimY + (j + offY) * dimX + i];
+ ctx->u[j * dimX * padZ + i * padZ + k] = tnv_ctx->uT[k * dimX * dimY + (j + offY) * dimX + i];
+ }
+ }
+ }
+
+ return 0;
+}
+
+static int tnv_finish(HWThread thr, void *hwctx, int device_id, void *data) {
+ int i,j,k;
+ tnv_context_t *tnv_ctx = (tnv_context_t*)data;
+ tnv_thread_t *ctx = tnv_ctx->thr_ctx + device_id;
+
+ int dimX = tnv_ctx->dimX;
+ int dimY = tnv_ctx->dimY;
+ int dimZ = tnv_ctx->dimZ;
+ int padZ = tnv_ctx->padZ;
+ int offY = ctx->offY;
+ int stepY = ctx->stepY;
+ int copY = ctx->copY;
+
+ for(k=0; k<dimZ; k++) {
+ for(j=0; j<stepY; j++) {
+ for(i=0; i<dimX; i++) {
+ tnv_ctx->uT[k * dimX * dimY + (j + offY) * dimX + i] = ctx->u[j * dimX * padZ + i * padZ + k];
+ }
+ }
+ }
+
+ return 0;
+}
+
+
+static int tnv_restore(HWThread thr, void *hwctx, int device_id, void *data) {
+ int i,j,k;
+ tnv_context_t *tnv_ctx = (tnv_context_t*)data;
+ tnv_thread_t *ctx = tnv_ctx->thr_ctx + device_id;
+
+ int dimX = tnv_ctx->dimX;
+ int dimY = tnv_ctx->dimY;
+ int dimZ = tnv_ctx->dimZ;
+ int stepY = ctx->stepY;
+ int copY = ctx->copY;
+ int padZ = tnv_ctx->padZ;
+ long DimTotal = (long)(dimX*stepY*padZ);
+ long Dim1Total = (long)(dimX*copY*padZ);
+
+ memset(ctx->u, 0, Dim1Total * sizeof(floatxx));
+ memset(ctx->qx, 0, DimTotal * sizeof(floatxx));
+ memset(ctx->qy, 0, DimTotal * sizeof(floatxx));
+ memset(ctx->gradx, 0, DimTotal * sizeof(floatxx));
+ memset(ctx->grady, 0, DimTotal * sizeof(floatxx));
+ memset(ctx->div, 0, Dim1Total * sizeof(floatxx));
+
+ return 0;
+}
+
+
+static int tnv_step(HWThread thr, void *hwctx, int device_id, void *data) {
+ long i, j, k, l, m;
+
+ tnv_context_t *tnv_ctx = (tnv_context_t*)data;
+ tnv_thread_t *ctx = tnv_ctx->thr_ctx + device_id;
+
+ int dimX = tnv_ctx->dimX;
+ int dimY = tnv_ctx->dimY;
+ int dimZ = tnv_ctx->dimZ;
+ int padZ = tnv_ctx->padZ;
+ int offY = ctx->offY;
+ int stepY = ctx->stepY;
+ int copY = ctx->copY;
+
+ floatxx *Input = ctx->Input;
+ floatxx *u = ctx->u;
+ floatxx *qx = ctx->qx;
+ floatxx *qy = ctx->qy;
+ floatxx *gradx = ctx->gradx;
+ floatxx *grady = ctx->grady;
+ floatxx *div = ctx->div;
+
+ long p = 1l;
+ long q = 1l;
+ long r = 0l;
+
+ float lambda = tnv_ctx->lambda;
+ float sigma = tnv_ctx->sigma;
+ float tau = tnv_ctx->tau;
+ float theta = tnv_ctx->theta;
+
+ float taulambda = tau * lambda;
+ float divtau = 1.0f / tau;
+ float divsigma = 1.0f / sigma;
+ float theta1 = 1.0f + theta;
+ float constant = 1.0f + taulambda;
+
+ float resprimal = 0.0f;
+ float resdual1 = 0.0f;
+ float resdual2 = 0.0f;
+ float product = 0.0f;
+ float unorm = 0.0f;
+ float qnorm = 0.0f;
+
+ floatyy qxdiff;
+ floatyy qydiff;
+ floatyy divdiff;
+ floatyy *gradxdiff = ctx->gradxdiff;
+ floatyy *gradydiff = ctx->gradydiff;
+ floatyy *ubarx = ctx->ubarx;
+ floatyy *ubary = ctx->ubary;
+ floatyy *udiff = ctx->udiff;
+
+ floatyy *udiff0 = ctx->udiff0;
+ floatyy *div0 = ctx->div0;
+
+
+ j = 0; {
+# define TNV_LOOP_FIRST_J
+ i = 0; {
+# define TNV_LOOP_FIRST_I
+# include "TNV_core_loop.h"
+# undef TNV_LOOP_FIRST_I
+ }
+ for(i = 1; i < (dimX - 1); i++) {
+# include "TNV_core_loop.h"
+ }
+ i = dimX - 1; {
+# define TNV_LOOP_LAST_I
+# include "TNV_core_loop.h"
+# undef TNV_LOOP_LAST_I
+ }
+# undef TNV_LOOP_FIRST_J
+ }
+
+
+
+ for(int j = 1; j < (copY - 1); j++) {
+ i = 0; {
+# define TNV_LOOP_FIRST_I
+# include "TNV_core_loop.h"
+# undef TNV_LOOP_FIRST_I
+ }
+ }
+
+ for(int j1 = 1; j1 < (copY - 1); j1 += BLOCK) {
+ for(int i1 = 1; i1 < (dimX - 1); i1 += BLOCK) {
+ for(int j2 = 0; j2 < BLOCK; j2 ++) {
+ j = j1 + j2;
+ for(int i2 = 0; i2 < BLOCK; i2++) {
+ i = i1 + i2;
+
+ if (i == (dimX - 1)) break;
+ if (j == (copY - 1)) { j2 = BLOCK; break; }
+# include "TNV_core_loop.h"
+ }
+ }
+ } // i
+
+ }
+
+ for(int j = 1; j < (copY - 1); j++) {
+ i = dimX - 1; {
+# define TNV_LOOP_LAST_I
+# include "TNV_core_loop.h"
+# undef TNV_LOOP_LAST_I
+ }
+ }
+
+
+
+ for (j = copY - 1; j < stepY; j++) {
+# define TNV_LOOP_LAST_J
+ i = 0; {
+# define TNV_LOOP_FIRST_I
+# include "TNV_core_loop.h"
+# undef TNV_LOOP_FIRST_I
+ }
+ for(i = 1; i < (dimX - 1); i++) {
+# include "TNV_core_loop.h"
+ }
+ i = dimX - 1; {
+# define TNV_LOOP_LAST_I
+# include "TNV_core_loop.h"
+# undef TNV_LOOP_LAST_I
+ }
+# undef TNV_LOOP_LAST_J
+ }
+
+
+
+ ctx->resprimal = resprimal;
+ ctx->resdual = resdual1 + resdual2;
+ ctx->product = product;
+ ctx->unorm = unorm;
+ ctx->qnorm = qnorm;
+
+ return 0;
+}
+
+static void TNV_CPU_init(float *InputT, float *uT, int dimX, int dimY, int dimZ) {
+ int i, off, size, err;
+
+ if (sched) return;
+
+ tnv_ctx.dimX = dimX;
+ tnv_ctx.dimY = dimY;
+ tnv_ctx.dimZ = dimZ;
+ // Padding seems actually slower
+// tnv_ctx.padZ = dimZ;
+// tnv_ctx.padZ = 4 * ((dimZ / 4) + ((dimZ % 4)?1:0));
+ tnv_ctx.padZ = 16 * ((dimZ / 16) + ((dimZ % 16)?1:0));
+
+ hw_sched_init();
+
+ int threads = hw_sched_get_cpu_count();
+ if (threads > dimY) threads = dimY/2;
+
+ int step = dimY / threads;
+ int extra = dimY % threads;
+
+ tnv_ctx.threads = threads;
+ tnv_ctx.thr_ctx = (tnv_thread_t*)calloc(threads, sizeof(tnv_thread_t));
+ for (i = 0, off = 0; i < threads; i++, off += size) {
+ tnv_thread_t *ctx = tnv_ctx.thr_ctx + i;
+ size = step + ((i < extra)?1:0);
+
+ ctx->offY = off;
+ ctx->stepY = size;
+
+ if (i == (threads-1)) ctx->copY = ctx->stepY;
+ else ctx->copY = ctx->stepY + 1;
+ }
+
+ sched = hw_sched_create(threads);
+ if (!sched) { fprintf(stderr, "Error creating threads\n"); exit(-1); }
+
+ err = hw_sched_schedule_thread_task(sched, (void*)&tnv_ctx, tnv_init);
+ if (!err) err = hw_sched_wait_task(sched);
+ if (err) { fprintf(stderr, "Error %i scheduling init threads", err); exit(-1); }
+}
+
+
+
+/*
+ * C-OMP implementation of Total Nuclear Variation regularisation model (2D + channels) [1]
+ * The code is modified from the implementation by Joan Duran <joan.duran@uib.es> see
+ * "denoisingPDHG_ipol.cpp" in Joans Collaborative Total Variation package
+ *
+ * Input Parameters:
+ * 1. Noisy volume of 2D + channel dimension, i.e. 3D volume
+ * 2. lambda - regularisation parameter
+ * 3. Number of iterations [OPTIONAL parameter]
+ * 4. eplsilon - tolerance constant [OPTIONAL parameter]
+ * 5. print information: 0 (off) or 1 (on) [OPTIONAL parameter]
+ *
+ * Output:
+ * 1. Filtered/regularized image (u)
+ *
+ * [1]. Duran, J., Moeller, M., Sbert, C. and Cremers, D., 2016. Collaborative total variation: a general framework for vectorial TV models. SIAM Journal on Imaging Sciences, 9(1), pp.116-151.
+ */
+
+float TNV_CPU_main(float *InputT, float *uT, float lambda, int maxIter, float tol, int dimX, int dimY, int dimZ)
+{
+ int err;
+ int iter;
+ int i,j,k,l,m;
+
+ lambda = 1.0f/(2.0f*lambda);
+ tnv_ctx.lambda = lambda;
+
+ // PDHG algorithm parameters
+ float tau = 0.5f;
+ float sigma = 0.5f;
+ float theta = 1.0f;
+
+ // Backtracking parameters
+ float s = 1.0f;
+ float gamma = 0.75f;
+ float beta = 0.95f;
+ float alpha0 = 0.2f;
+ float alpha = alpha0;
+ float delta = 1.5f;
+ float eta = 0.95f;
+
+ TNV_CPU_init(InputT, uT, dimX, dimY, dimZ);
+
+ tnv_ctx.InputT = InputT;
+ tnv_ctx.uT = uT;
+
+ int padZ = tnv_ctx.padZ;
+
+ err = hw_sched_schedule_thread_task(sched, (void*)&tnv_ctx, tnv_start);
+ if (!err) err = hw_sched_wait_task(sched);
+ if (err) { fprintf(stderr, "Error %i scheduling start threads", err); exit(-1); }
+
+
+ // Apply Primal-Dual Hybrid Gradient scheme
+ float residual = fLarge;
+ int started = 0;
+ for(iter = 0; iter < maxIter; iter++) {
+ float resprimal = 0.0f;
+ float resdual = 0.0f;
+ float product = 0.0f;
+ float unorm = 0.0f;
+ float qnorm = 0.0f;
+
+ float divtau = 1.0f / tau;
+
+ tnv_ctx.sigma = sigma;
+ tnv_ctx.tau = tau;
+ tnv_ctx.theta = theta;
+
+ err = hw_sched_schedule_thread_task(sched, (void*)&tnv_ctx, tnv_step);
+ if (!err) err = hw_sched_wait_task(sched);
+ if (err) { fprintf(stderr, "Error %i scheduling tnv threads", err); exit(-1); }
+
+ // border regions
+ for (j = 1; j < tnv_ctx.threads; j++) {
+ tnv_thread_t *ctx0 = tnv_ctx.thr_ctx + (j - 1);
+ tnv_thread_t *ctx = tnv_ctx.thr_ctx + j;
+
+ m = (ctx0->stepY - 1) * dimX * padZ;
+ for(i = 0; i < dimX; i++) {
+ for(k = 0; k < dimZ; k++) {
+ int l = i * padZ + k;
+
+ floatyy divdiff = ctx->div0[l] - ctx->div[l];
+ floatyy udiff = ctx->udiff0[l];
+
+ ctx->div[l] -= ctx0->qy[l + m];
+ ctx0->div[m + l + dimX*padZ] = ctx->div[l];
+ ctx0->u[m + l + dimX*padZ] = ctx->u[l];
+
+ divdiff += ctx0->qy[l + m];
+ resprimal += fabs(divtau * udiff + divdiff);
+ }
+ }
+ }
+
+ {
+ tnv_thread_t *ctx = tnv_ctx.thr_ctx + 0;
+ for(i = 0; i < dimX; i++) {
+ for(k = 0; k < dimZ; k++) {
+ int l = i * padZ + k;
+
+ floatyy divdiff = ctx->div0[l] - ctx->div[l];
+ floatyy udiff = ctx->udiff0[l];
+ resprimal += fabs(divtau * udiff + divdiff);
+ }
+ }
+ }
+
+ for (j = 0; j < tnv_ctx.threads; j++) {
+ tnv_thread_t *ctx = tnv_ctx.thr_ctx + j;
+ resprimal += ctx->resprimal;
+ resdual += ctx->resdual;
+ product += ctx->product;
+ unorm += ctx->unorm;
+ qnorm += ctx->qnorm;
+ }
+
+ residual = (resprimal + resdual) / ((float) (dimX*dimY*dimZ));
+ float b = (2.0f * tau * sigma * product) / (gamma * sigma * unorm + gamma * tau * qnorm);
+ float dual_dot_delta = resdual * s * delta;
+ float dual_div_delta = (resdual * s) / delta;
+// printf("resprimal: %f, resdual: %f, b: %f (product: %f, unorm: %f, qnorm: %f)\n", resprimal, resdual, b, product, unorm, qnorm);
+
+
+ if(b > 1) {
+
+ // Decrease step-sizes to fit balancing principle
+ tau = (beta * tau) / b;
+ sigma = (beta * sigma) / b;
+ alpha = alpha0;
+
+ if (started) {
+ fprintf(stderr, "\n\n\nWARNING: Back-tracking is required in the middle of iterative optimization! We CAN'T do it in the fast version. The standard TNV recommended\n\n\n");
+ } else {
+ err = hw_sched_schedule_thread_task(sched, (void*)&tnv_ctx, tnv_restore);
+ if (!err) err = hw_sched_wait_task(sched);
+ if (err) { fprintf(stderr, "Error %i scheduling restore threads", err); exit(-1); }
+ }
+ } else {
+ started = 1;
+ if(resprimal > dual_dot_delta) {
+ // Increase primal step-size and decrease dual step-size
+ tau = tau / (1.0f - alpha);
+ sigma = sigma * (1.0f - alpha);
+ alpha = alpha * eta;
+ } else if(resprimal < dual_div_delta) {
+ // Decrease primal step-size and increase dual step-size
+ tau = tau * (1.0f - alpha);
+ sigma = sigma / (1.0f - alpha);
+ alpha = alpha * eta;
+ }
+ }
+
+ if (residual < tol) break;
+ }
+
+ err = hw_sched_schedule_thread_task(sched, (void*)&tnv_ctx, tnv_finish);
+ if (!err) err = hw_sched_wait_task(sched);
+ if (err) { fprintf(stderr, "Error %i scheduling finish threads", err); exit(-1); }
+
+
+ printf("Iterations stopped at %i with the residual %f \n", iter, residual);
+// printf("Return: %f\n", *uT);
+
+ return *uT;
+}
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