diff --git a/cpp/ISAM2-inl.h b/cpp/ISAM2-inl.h
index 8622fe976..added3f52 100644
--- a/cpp/ISAM2-inl.h
+++ b/cpp/ISAM2-inl.h
@@ -23,7 +23,7 @@ namespace gtsam {
 	using namespace std;
 
 	// from inference-inl.h - need to additionally return the newly created factor for caching
-	boost::shared_ptr<GaussianConditional> _eliminateOne(FactorGraph<GaussianFactor>& graph, cachedFactors& cached, const string& key) {
+	boost::shared_ptr<GaussianConditional> _eliminateOne(FactorGraph<GaussianFactor>& graph, cachedFactors& cached, const Symbol& key) {
 
 		// combine the factors of all nodes connected to the variable to be eliminated
 		// if no factors are connected to key, returns an empty factor
@@ -47,7 +47,7 @@ namespace gtsam {
 	// from GaussianFactorGraph.cpp, see _eliminateOne above
 	GaussianBayesNet _eliminate(FactorGraph<GaussianFactor>& graph, cachedFactors& cached, const Ordering& ordering) {
 		GaussianBayesNet chordalBayesNet; // empty
-		BOOST_FOREACH(string key, ordering) {
+		BOOST_FOREACH(const Symbol& key, ordering) {
 			GaussianConditional::shared_ptr cg = _eliminateOne(graph, cached, key);
 			chordalBayesNet.push_back(cg);
 		}
@@ -67,7 +67,7 @@ namespace gtsam {
 	/** Create a Bayes Tree from a nonlinear factor graph */
 	template<class Conditional, class Config>
 	ISAM2<Conditional, Config>::ISAM2(const NonlinearFactorGraph<Config>& nlfg, const Ordering& ordering, const Config& config)
-	: BayesTree<Conditional>(nlfg.linearize(config).eliminate(ordering)), nonlinearFactors_(nlfg), config_(config) {
+	: BayesTree<Conditional>(nlfg.linearize(config).eliminate(ordering)), nonlinearFactors_(nlfg), linPoint_(config), estimate_(config) {
 		// todo: repeats calculation above, just to set "cached"
 		_eliminate_const(nlfg.linearize(config), cached, ordering);
 	}
@@ -76,36 +76,41 @@ namespace gtsam {
 	// retrieve all factors that ONLY contain the affected variables
 	// (note that the remaining stuff is summarized in the cached factors)
 	template<class Conditional, class Config>
-	FactorGraph<GaussianFactor> ISAM2<Conditional, Config>::relinearizeAffectedFactors(const list<string>& affectedKeys) {
+	FactorGraph<GaussianFactor> ISAM2<Conditional, Config>::relinearizeAffectedFactors(const list<Symbol>& affectedKeys) {
+
 		NonlinearFactorGraph<Config> nonlinearAffectedFactors;
+
 		typename FactorGraph<NonlinearFactor<Config> >::iterator it;
 		for(it = nonlinearFactors_.begin(); it != nonlinearFactors_.end(); it++) {
 			bool inside = true;
-			BOOST_FOREACH(string key, (*it)->keys()) {
+			BOOST_FOREACH(const Symbol& key, (*it)->keys()) {
 				if (find(affectedKeys.begin(), affectedKeys.end(), key) == affectedKeys.end())
 					inside = false;
 			}
 			if (inside)
 				nonlinearAffectedFactors.push_back(*it);
 		}
-		return nonlinearAffectedFactors.linearize(config_);
+
+		return nonlinearAffectedFactors.linearize(linPoint_);
 	}
 
 	/* ************************************************************************* */
+	// find intermediate (linearized) factors from cache that are passed into the affected area
 	template<class Conditional, class Config>
 	FactorGraph<GaussianFactor> ISAM2<Conditional, Config>::getCachedBoundaryFactors(Cliques& orphans) {
-		// add intermediate (linearized) factors from cache that are passed into the affected area
 		FactorGraph<GaussianFactor> cachedBoundary;
+
 		BOOST_FOREACH(sharedClique orphan, orphans) {
 			// find the last variable that is not part of the separator
 			string oneTooFar = orphan->separator_.front();
-			list<string> keys = orphan->keys();
-			list<string>::iterator it = find(keys.begin(), keys.end(), oneTooFar);
+			list<Symbol> keys = orphan->keys();
+			list<Symbol>::iterator it = find(keys.begin(), keys.end(), oneTooFar);
 			it--;
-			string key = *it;
+			const Symbol& key = *it;
 			// retrieve the cached factor and add to boundary
 			cachedBoundary.push_back(cached[key]);
 		}
+
 		return cachedBoundary;
 	}
 
@@ -116,20 +121,21 @@ namespace gtsam {
 
 		// copy variables into config_, but don't overwrite existing entries (current linearization point!)
 		for (typename Config::const_iterator it = config.begin(); it!=config.end(); it++) {
-			if (!config_.contains(it->first)) {
-				config_.insert(it->first, it->second);
+			if (!linPoint_.contains(it->first)) {
+				linPoint_.insert(it->first, it->second);
+				estimate_.insert(it->first, it->second);
 			}
 		}
 
-		FactorGraph<GaussianFactor> newFactorsLinearized = newFactors.linearize(config_);
+		FactorGraph<GaussianFactor> newFactorsLinearized = newFactors.linearize(linPoint_);
 
 		// Remove the contaminated part of the Bayes tree
 		FactorGraph<GaussianFactor> affectedFactors;
 		boost::tie(affectedFactors, orphans) = this->removeTop(newFactorsLinearized);
 
 		// relinearize the affected factors ...
-		list<string> affectedKeys = affectedFactors.keys();
-		FactorGraph<GaussianFactor> factors = relinearizeAffectedFactors(affectedKeys);
+		list<Symbol> affectedKeys = affectedFactors.keys();
+		FactorGraph<GaussianFactor> factors = relinearizeAffectedFactors(affectedKeys); // todo: searches through all factors, potentially expensive
 
 		// ... add the cached intermediate results from the boundary of the orphans ...
 		FactorGraph<GaussianFactor> cachedBoundary = getCachedBoundaryFactors(orphans);
@@ -143,7 +149,7 @@ namespace gtsam {
 		if (true) {
 			ordering = factors.getOrdering();
 		} else {
-			list<string> keys = factors.keys();
+			list<Symbol> keys = factors.keys();
 			keys.sort(); // todo: correct sorting order?
 			ordering = keys;
 		}
@@ -162,16 +168,17 @@ namespace gtsam {
 		// add orphans to the bottom of the new tree
 		BOOST_FOREACH(sharedClique orphan, orphans) {
 
-			string key = orphan->separator_.front();
+		  Symbol key = orphan->separator_.front();
 			sharedClique parent = (*this)[key];
 
 			parent->children_ += orphan;
 			orphan->parent_ = parent; // set new parent!
 		}
 
-		// update solution
-		VectorConfig solution = optimize2(*this);
-		solution.print();
+		// update solution - todo: potentially expensive
+		VectorConfig delta = optimize2(*this);
+//		delta.print();
+		estimate_ += delta;
 
 	}