Using vector instead of deque in VariableIndex, BayesTree::Nodes, and GaussianISAM::Dims. In practice it appears to be faster due to smart reallocation strategies (still need to investigate whether we should use reserve, resize, or neither).

gtsam/inference/BayesTree.h

@@ -92,7 +92,7 @@ namespace gtsam {
     };
 
     /** Map from indices to Clique */
-    typedef std::deque<sharedClique> Nodes;
+    typedef std::vector<sharedClique> Nodes;
 
   protected:
 

gtsam/inference/VariableIndex.cpp

@@ -68,7 +68,7 @@ void VariableIndex::outputMetisFormat(ostream& os) const {
 /* ************************************************************************* */
 void VariableIndex::permuteInPlace(const Permutation& permutation) {
   // Create new index and move references to data into it in permuted order
-  deque<VariableIndex::Factors> newIndex(this->size());
+  vector<VariableIndex::Factors> newIndex(this->size());
   for(Index i = 0; i < newIndex.size(); ++i)
     newIndex[i].swap(this->index_[permutation[i]]);
 

gtsam/inference/VariableIndex.h

@@ -48,7 +48,7 @@ public:
   typedef Factors::const_iterator Factor_const_iterator;
 
 protected:
-  std::deque<Factors> index_;
+  std::vector<Factors> index_;
   size_t nFactors_; // Number of factors in the original factor graph.
   size_t nEntries_; // Sum of involved variable counts of each factor.
 

gtsam/linear/GaussianISAM.h

@@ -30,11 +30,11 @@ namespace gtsam {
 class GaussianISAM : public ISAM<GaussianConditional> {
 
   typedef ISAM<GaussianConditional> Super;
-  std::deque<size_t, boost::fast_pool_allocator<size_t> > dims_;
+  std::vector<size_t> dims_;
 
 public:
 
-  typedef std::deque<size_t, boost::fast_pool_allocator<size_t> > Dims;
+  typedef std::vector<size_t> Dims;
 
   /** Create an empty Bayes Tree */
   GaussianISAM() : Super() {}