vppinfra: move unused code to extras/deprecated/vppinfra

Type: improvement

Signed-off-by: Dave Barach <dave@barachs.net>
Change-Id: Id28299a188feefa1899d835fd499f018af95d81b

1 files changed, 0 insertions, 172 deletions

diff --git a/src/vppinfra/anneal.c b/src/vppinfra/anneal.c
deleted file mode 100644
index 35d10946482..00000000000
--- a/src/vppinfra/anneal.c
+++ /dev/null
@@ -1,172 +0,0 @@
-/*
-  Copyright (c) 2011 Cisco and/or its affiliates.
-
-  * 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 <vppinfra/anneal.h>
-
-/*
- * Optimize an objective function by simulated annealing
- *
- * Here are a couple of short, easily-understood
- * descriptions of simulated annealing:
- *
- * http://www.cs.sandia.gov/opt/survey/sa.html
- * Numerical Recipes in C, 2nd ed., 444ff
- *
- * The description in the Wikipedia is not helpful.
- *
- * The algorithm tries to produce a decent answer to combinatorially
- * explosive optimization problems by analogy to slow cooling
- * of hot metal, aka annealing.
- *
- * There are (at least) three problem-dependent annealing parameters
- * to consider:
- *
- * t0, the initial "temperature. Should be set so that the probability
- * of accepting a transition to a higher cost configuration is
- * initially about 0.8.
- *
- * ntemps, the number of temperatures to use. Each successive temperature
- * is some fraction of the previous temperature.
- *
- * nmoves_per_temp, the number of configurations to try at each temperature
- *
- * It is a black art to set ntemps, nmoves_per_temp, and the rate
- * at which the temperature drops. Go too fast with too few iterations,
- * and the computation falls into a local minimum instead of the
- * (desired) global minimum.
- */
-
-void
-clib_anneal (clib_anneal_param_t * p)
-{
-  f64 t;
-  f64 cost, prev_cost, delta_cost, initial_cost, best_cost;
-  f64 random_accept, delta_cost_over_t;
-  f64 total_increase = 0.0, average_increase;
-  u32 i, j;
-  u32 number_of_increases = 0;
-  u32 accepted_this_temperature;
-  u32 best_saves_this_temperature;
-  int accept;
-
-  t = p->initial_temperature;
-  best_cost = initial_cost = prev_cost = p->anneal_metric (p->opaque);
-  p->anneal_save_best_configuration (p->opaque);
-
-  if (p->flags & CLIB_ANNEAL_VERBOSE)
-    fformat (stdout, "Initial cost %.2f\n", initial_cost);
-
-  for (i = 0; i < p->number_of_temperatures; i++)
-    {
-      accepted_this_temperature = 0;
-      best_saves_this_temperature = 0;
-
-      p->anneal_restore_best_configuration (p->opaque);
-      cost = best_cost;
-
-      for (j = 0; j < p->number_of_configurations_per_temperature; j++)
-	{
-	  p->anneal_new_configuration (p->opaque);
-	  cost = p->anneal_metric (p->opaque);
-
-	  delta_cost = cost - prev_cost;
-
-	  /* cost function looks better, accept this move */
-	  if (p->flags & CLIB_ANNEAL_MINIMIZE)
-	    accept = delta_cost < 0.0;
-	  else
-	    accept = delta_cost > 0.0;
-
-	  if (accept)
-	    {
-	      if (p->flags & CLIB_ANNEAL_MINIMIZE)
-		if (cost < best_cost)
-		  {
-		    if (p->flags & CLIB_ANNEAL_VERBOSE)
-		      fformat (stdout, "New best cost %.2f\n", cost);
-		    best_cost = cost;
-		    p->anneal_save_best_configuration (p->opaque);
-		    best_saves_this_temperature++;
-		  }
-
-	      accepted_this_temperature++;
-	      prev_cost = cost;
-	      continue;
-	    }
-
-	  /* cost function worse, keep stats to suggest t0 */
-	  total_increase += (p->flags & CLIB_ANNEAL_MINIMIZE) ?
-	    delta_cost : -delta_cost;
-
-	  number_of_increases++;
-
-	  /*
-	   * Accept a higher cost with Pr { e^(-(delta_cost / T)) },
-	   * equivalent to rnd[0,1] < e^(-(delta_cost / T))
-	   *
-	   * AKA, the Boltzmann factor.
-	   */
-	  random_accept = random_f64 (&p->random_seed);
-
-	  delta_cost_over_t = delta_cost / t;
-
-	  if (random_accept < exp (-delta_cost_over_t))
-	    {
-	      accepted_this_temperature++;
-	      prev_cost = cost;
-	      continue;
-	    }
-	  p->anneal_restore_previous_configuration (p->opaque);
-	}
-
-      if (p->flags & CLIB_ANNEAL_VERBOSE)
-	{
-	  fformat (stdout, "Temp %.2f, cost %.2f, accepted %d, bests %d\n", t,
-		   prev_cost, accepted_this_temperature,
-		   best_saves_this_temperature);
-	  fformat (stdout, "Improvement %.2f\n", initial_cost - prev_cost);
-	  fformat (stdout, "-------------\n");
-	}
-
-      t = t * p->temperature_step;
-    }
-
-  /*
-   * Empirically, one wants the probability of accepting a move
-   * at the initial temperature to be about 0.8.
-   */
-  average_increase = total_increase / (f64) number_of_increases;
-  p->suggested_initial_temperature = average_increase / 0.22;	/* 0.22 = -ln (0.8) */
-
-  p->final_temperature = t;
-  p->final_metric = p->anneal_metric (p->opaque);
-
-  if (p->flags & CLIB_ANNEAL_VERBOSE)
-    {
-      fformat (stdout, "Average cost increase from a bad move: %.2f\n",
-	       average_increase);
-      fformat (stdout, "Suggested t0 = %.2f\n",
-	       p->suggested_initial_temperature);
-    }
-}
-
-/*
- * fd.io coding-style-patch-verification: ON
- *
- * Local Variables:
- * eval: (c-set-style "gnu")
- * End:
- */