diff --git a/gtsam/base/Matrix.cpp b/gtsam/base/Matrix.cpp
index 050f66ff0..57a45d4a1 100644
--- a/gtsam/base/Matrix.cpp
+++ b/gtsam/base/Matrix.cpp
@@ -26,8 +26,8 @@
 #include <boost/foreach.hpp>
 #include <boost/tuple/tuple.hpp>
 
-#include <stdarg.h>
-#include <string.h>
+#include <cstdarg>
+#include <cstring>
 #include <iomanip>
 #include <list>
 #include <fstream>
diff --git a/gtsam/base/Vector.cpp b/gtsam/base/Vector.cpp
index 2d319961d..98ad43e05 100644
--- a/gtsam/base/Vector.cpp
+++ b/gtsam/base/Vector.cpp
@@ -16,7 +16,7 @@
  * @author  Frank Dellaert
  */
 
-#include <stdarg.h>
+#include <cstdarg>
 #include <limits>
 #include <iostream>
 #include <fstream>
@@ -25,11 +25,7 @@
 #include <cmath>
 #include <boost/foreach.hpp>
 #include <boost/optional.hpp>
-#include <stdio.h>
-
-#ifdef WIN32
-#include <Windows.h>
-#endif
+#include <cstdio>
 
 #include <boost/random/normal_distribution.hpp>
 #include <boost/random/variate_generator.hpp>
@@ -37,6 +33,10 @@
 #include <gtsam/base/Vector.h>
 #include <gtsam/base/types.h>
 
+//#ifdef WIN32
+//#include <Windows.h>
+//#endif
+
 using namespace std;
 
 boost::minstd_rand generator(42u);
@@ -56,11 +56,11 @@ void odprintf_(const char *format, ostream& stream, ...) {
 #endif
 	va_end(args);
 
-#ifdef WIN32
-	OutputDebugString(buf);
-#else
+//#ifdef WIN32
+//	OutputDebugString(buf);
+//#else
 	stream << buf;
-#endif
+//#endif
 }
 
 /* ************************************************************************* */
@@ -77,11 +77,11 @@ void odprintf(const char *format, ...) {
 #endif
 	va_end(args);
 
-#ifdef WIN32
-	OutputDebugString(buf);
-#else
+//#ifdef WIN32
+//	OutputDebugString(buf);
+//#else
 	cout << buf;
-#endif
+//#endif
 }
 
 /* ************************************************************************* */
diff --git a/gtsam/base/Vector.h b/gtsam/base/Vector.h
index 84fd506cb..b4be74f4e 100644
--- a/gtsam/base/Vector.h
+++ b/gtsam/base/Vector.h
@@ -22,6 +22,7 @@
 
 #include <list>
 #include <vector>
+#include <gtsam/base/types.h>
 #include <gtsam/3rdparty/Eigen/Eigen/Core>
 #include <boost/random/linear_congruential.hpp>
 
diff --git a/gtsam/base/types.h b/gtsam/base/types.h
index 6106f49fd..e9264d516 100644
--- a/gtsam/base/types.h
+++ b/gtsam/base/types.h
@@ -84,4 +84,12 @@ using boost::math::isinf;
 #endif
 
 #endif
+
+#ifdef min
+#undef min
+#endif
+
+#ifdef max
+#undef max
+#endif
 
diff --git a/gtsam/discrete/Signature.cpp b/gtsam/discrete/Signature.cpp
index ee7c1e59a..439f20864 100644
--- a/gtsam/discrete/Signature.cpp
+++ b/gtsam/discrete/Signature.cpp
@@ -33,6 +33,7 @@ namespace gtsam {
 
 #ifdef BOOST_HAVE_PARSER
 	namespace qi = boost::spirit::qi;
+  namespace ph = boost::phoenix;
 
 	// parser for strings of form "99/1 80/20" etc...
 	namespace parser {
@@ -85,9 +86,9 @@ namespace gtsam {
 			// check for OR, AND on whole phrase
 			It f = spec.begin(), l = spec.end();
 			if (qi::parse(f, l,
-						qi::lit("OR")[ref(table) = logic(false, true, true, true)]) ||
+						qi::lit("OR")[ph::ref(table) = logic(false, true, true, true)]) ||
 					qi::parse(f, l,
-						qi::lit("AND")[ref(table) = logic(false, false, false, true)]))
+						qi::lit("AND")[ph::ref(table) = logic(false, false, false, true)]))
 				return true;
 
 			// tokenize into separate rows
@@ -97,9 +98,9 @@ namespace gtsam {
 				Signature::Row values;
 				It tf = token.begin(), tl = token.end();
 				bool r = qi::parse(tf, tl,
-						qi::double_[push_back(ref(values), qi::_1)] >> +("/" >> qi::double_[push_back(ref(values), qi::_1)]) |
-						qi::lit("T")[ref(values) = T] |
-						qi::lit("F")[ref(values) = F] );
+						qi::double_[push_back(ph::ref(values), qi::_1)] >> +("/" >> qi::double_[push_back(ph::ref(values), qi::_1)]) |
+						qi::lit("T")[ph::ref(values) = T] |
+						qi::lit("F")[ph::ref(values) = F] );
 				if (!r)
 					return false;
 				table.push_back(values);
diff --git a/gtsam/geometry/Point3.h b/gtsam/geometry/Point3.h
index 9eeb748db..7f7845dfb 100644
--- a/gtsam/geometry/Point3.h
+++ b/gtsam/geometry/Point3.h
@@ -21,6 +21,7 @@
 
 #pragma once
 
+#include <cmath>
 #include <boost/serialization/nvp.hpp>
 
 #include <gtsam/base/Matrix.h>
@@ -150,7 +151,7 @@ namespace gtsam {
 
     /** distance between two points */
     double dist(const Point3& p2) const {
-      return sqrt(pow(x()-p2.x(),2.0) + pow(y()-p2.y(),2.0) + pow(z()-p2.z(),2.0));
+      return std::sqrt(pow(x()-p2.x(),2.0) + pow(y()-p2.y(),2.0) + pow(z()-p2.z(),2.0));
     }
 
     /** Distance of the point from the origin */
diff --git a/gtsam/inference/BayesTree-inl.h b/gtsam/inference/BayesTree-inl.h
index 95b070739..00cd38609 100644
--- a/gtsam/inference/BayesTree-inl.h
+++ b/gtsam/inference/BayesTree-inl.h
@@ -53,7 +53,7 @@ namespace gtsam {
 
 	template<class CONDITIONAL, class CLIQUE>
 	void BayesTree<CONDITIONAL,CLIQUE>::getCliqueData(CliqueData& data,
-			BayesTree<CONDITIONAL,CLIQUE>::sharedClique clique) const {
+			typename BayesTree<CONDITIONAL,CLIQUE>::sharedClique clique) const {
 		data.conditionalSizes.push_back((*clique)->nrFrontals());
 		data.separatorSizes.push_back((*clique)->nrParents());
 		BOOST_FOREACH(sharedClique c, clique->children_) {
@@ -74,7 +74,7 @@ namespace gtsam {
 
 	template<class CONDITIONAL, class CLIQUE>
 	void BayesTree<CONDITIONAL,CLIQUE>::saveGraph(ostream &s,
-			BayesTree<CONDITIONAL,CLIQUE>::sharedClique clique,
+			typename BayesTree<CONDITIONAL,CLIQUE>::sharedClique clique,
 			int parentnum) const {
 		static int num = 0;
 		bool first = true;
diff --git a/gtsam/inference/ClusterTree-inl.h b/gtsam/inference/ClusterTree-inl.h
index 0f1f823af..0a0b9e64e 100644
--- a/gtsam/inference/ClusterTree-inl.h
+++ b/gtsam/inference/ClusterTree-inl.h
@@ -58,7 +58,7 @@ namespace gtsam {
 
 	/* ************************************************************************* */
 	template<class FG>
-	bool ClusterTree<FG>::Cluster::equals(const ClusterTree<FG>::Cluster& other) const {
+	bool ClusterTree<FG>::Cluster::equals(const Cluster& other) const {
 		if (frontal != other.frontal) return false;
 		if (separator != other.separator) return false;
 		if (children_.size() != other.children_.size()) return false;
diff --git a/gtsam/inference/GenericMultifrontalSolver-inl.h b/gtsam/inference/GenericMultifrontalSolver-inl.h
index 652db02ca..b24db8484 100644
--- a/gtsam/inference/GenericMultifrontalSolver-inl.h
+++ b/gtsam/inference/GenericMultifrontalSolver-inl.h
@@ -49,15 +49,14 @@ namespace gtsam {
 	}
 
 	/* ************************************************************************* */
-	template<class F, class JT>
-	typename BayesTree<typename F::ConditionalType>::shared_ptr GenericMultifrontalSolver<F, JT>::eliminate(
-			typename FactorGraph<F>::Eliminate function) const {
+	template<class FACTOR, class JUNCTIONTREE>
+	typename BayesTree<typename FACTOR::ConditionalType>::shared_ptr GenericMultifrontalSolver<FACTOR, JUNCTIONTREE>::eliminate(Eliminate function) const {
 
 		// eliminate junction tree, returns pointer to root
-		typename BayesTree<typename F::ConditionalType>::sharedClique root = junctionTree_->eliminate(function);
+		typename BayesTree<typename FACTOR::ConditionalType>::sharedClique root = junctionTree_->eliminate(function);
 
 		// create an empty Bayes tree and insert root clique
-		typename BayesTree<typename F::ConditionalType>::shared_ptr bayesTree(new BayesTree<typename F::ConditionalType>);
+		typename BayesTree<typename FACTOR::ConditionalType>::shared_ptr bayesTree(new BayesTree<typename FACTOR::ConditionalType>);
 		bayesTree->insert(root);
 
 		// return the Bayes tree
diff --git a/gtsam/linear/GaussianConditional.cpp b/gtsam/linear/GaussianConditional.cpp
index de459a00f..c048c8dd8 100644
--- a/gtsam/linear/GaussianConditional.cpp
+++ b/gtsam/linear/GaussianConditional.cpp
@@ -73,7 +73,7 @@ GaussianConditional::GaussianConditional(Index key, const Vector& d, const Matri
 			const Matrix& R, const list<pair<Index, Matrix> >& parents, const Vector& sigmas) :
 		IndexConditional(key, GetKeys(parents.size(), parents.begin(), parents.end())), rsd_(matrix_), sigmas_(sigmas) {
   assert(R.rows() <= R.cols());
-  size_t dims[1+parents.size()+1];
+  size_t* dims = (size_t*)alloca(sizeof(size_t)*(1+parents.size()+1)); // FIXME: alloca is bad, just ask Google.
   dims[0] = R.cols();
   size_t j=1;
   std::list<std::pair<Index, Matrix> >::const_iterator parent=parents.begin();
@@ -95,7 +95,7 @@ GaussianConditional::GaussianConditional(const std::list<std::pair<Index, Matrix
     const size_t nrFrontals, const Vector& d, const Vector& sigmas) :
     IndexConditional(GetKeys(terms.size(), terms.begin(), terms.end()), nrFrontals),
     rsd_(matrix_), sigmas_(sigmas) {
-  size_t dims[terms.size()+1];
+  size_t* dims = (size_t*)alloca(sizeof(size_t)*(terms.size()+1)); // FIXME: alloca is bad, just ask Google.
   size_t j=0;
   typedef pair<Index, Matrix> Index_Matrix;
   BOOST_FOREACH(const Index_Matrix& term, terms) {
diff --git a/gtsam/linear/HessianFactor.cpp b/gtsam/linear/HessianFactor.cpp
index b1f829005..ebe94514b 100644
--- a/gtsam/linear/HessianFactor.cpp
+++ b/gtsam/linear/HessianFactor.cpp
@@ -188,7 +188,7 @@ HessianFactor::HessianFactor(const std::vector<Index>& js, const std::vector<Mat
   }
 
   // Create the dims vector
-  size_t dims[variable_count+1];
+  size_t* dims = (size_t*)alloca(sizeof(size_t)*(variable_count+1)); // FIXME: alloca is bad, just ask Google.
   size_t total_size = 0;
   for(unsigned int i = 0; i < variable_count; ++i){
     dims[i] = gs[i].size();
@@ -345,7 +345,7 @@ void HessianFactor::updateATA(const HessianFactor& update, const Scatter& scatte
 
 	// First build an array of slots
 	tic(1, "slots");
-	size_t slots[update.size()];
+	size_t* slots = (size_t*)alloca(sizeof(size_t)*update.size()); // FIXME: alloca is bad, just ask Google.
 	size_t slot = 0;
 	BOOST_FOREACH(Index j, update) {
 		slots[slot] = scatter.find(j)->second.slot;
@@ -399,7 +399,7 @@ void HessianFactor::updateATA(const JacobianFactor& update, const Scatter& scatt
 
 	// First build an array of slots
 	tic(1, "slots");
-	size_t slots[update.size()];
+	size_t* slots = (size_t*)alloca(sizeof(size_t)*update.size()); // FIXME: alloca is bad, just ask Google.
 	size_t slot = 0;
 	BOOST_FOREACH(Index j, update) {
 		slots[slot] = scatter.find(j)->second.slot;
diff --git a/gtsam/linear/JacobianFactor.cpp b/gtsam/linear/JacobianFactor.cpp
index e2facec40..37fd74c39 100644
--- a/gtsam/linear/JacobianFactor.cpp
+++ b/gtsam/linear/JacobianFactor.cpp
@@ -118,7 +118,7 @@ namespace gtsam {
   	GaussianFactor(GetKeys(terms.size(), terms.begin(), terms.end())),
 		model_(model), firstNonzeroBlocks_(b.size(), 0), Ab_(matrix_)
   {
-    size_t dims[terms.size()+1];
+    size_t* dims = (size_t*)alloca(sizeof(size_t)*(terms.size()+1)); // FIXME: alloca is bad, just ask Google.
     for(size_t j=0; j<terms.size(); ++j)
       dims[j] = terms[j].second.cols();
     dims[terms.size()] = 1;
@@ -135,7 +135,7 @@ namespace gtsam {
       GaussianFactor(GetKeys(terms.size(), terms.begin(), terms.end())),
     model_(model), firstNonzeroBlocks_(b.size(), 0), Ab_(matrix_)
   {
-    size_t dims[terms.size()+1];
+    size_t* dims=(size_t*)alloca(sizeof(size_t)*(terms.size()+1)); // FIXME: alloca is bad, just ask Google.
     size_t j=0;
     std::list<std::pair<Index, Matrix> >::const_iterator term=terms.begin();
     for(; term!=terms.end(); ++term,++j)
@@ -488,7 +488,7 @@ namespace gtsam {
 			size_t>& varDims, size_t m) {
 		keys_.resize(variableSlots.size());
 		std::transform(variableSlots.begin(), variableSlots.end(), begin(),
-				bind(&VariableSlots::const_iterator::value_type::first,
+				boost::bind(&VariableSlots::const_iterator::value_type::first,
 						boost::lambda::_1));
 		varDims.push_back(1);
 		Ab_.copyStructureFrom(BlockAb(matrix_, varDims.begin(), varDims.end(), m));
diff --git a/gtsam/linear/NoiseModel.h b/gtsam/linear/NoiseModel.h
index b6cf4deac..87391be0a 100644
--- a/gtsam/linear/NoiseModel.h
+++ b/gtsam/linear/NoiseModel.h
@@ -20,6 +20,7 @@
 
 #include <boost/serialization/nvp.hpp>
 #include <gtsam/base/Matrix.h>
+#include <cmath>
 
 namespace gtsam {
 
@@ -609,7 +610,7 @@ namespace gtsam {
 		  virtual bool equals(const Base& expected, const double tol=1e-8) const = 0;
 
 		  inline double sqrtWeight(const double &error) const
-		  { return sqrt(weight(error)); }
+		  { return std::sqrt(weight(error)); }
 
       /** produce a weight vector according to an error vector and the implemented
        * robust function */
diff --git a/gtsam/nonlinear/DoglegOptimizerImpl.cpp b/gtsam/nonlinear/DoglegOptimizerImpl.cpp
index 480d76f6a..ccfdf781a 100644
--- a/gtsam/nonlinear/DoglegOptimizerImpl.cpp
+++ b/gtsam/nonlinear/DoglegOptimizerImpl.cpp
@@ -15,6 +15,7 @@
  * @author  Richard Roberts
  */
 
+#include <cmath>
 #include <gtsam/nonlinear/DoglegOptimizerImpl.h>
 
 namespace gtsam {
@@ -27,12 +28,12 @@ VectorValues DoglegOptimizerImpl::ComputeDoglegPoint(
   double DeltaSq = Delta*Delta;
   double x_u_norm_sq = dx_u.vector().squaredNorm();
   double x_n_norm_sq = dx_n.vector().squaredNorm();
-  if(verbose) cout << "Steepest descent magnitude " << sqrt(x_u_norm_sq) << ", Newton's method magnitude " << sqrt(x_n_norm_sq) << endl;
+  if(verbose) cout << "Steepest descent magnitude " << std::sqrt(x_u_norm_sq) << ", Newton's method magnitude " << std::sqrt(x_n_norm_sq) << endl;
   if(DeltaSq < x_u_norm_sq) {
     // Trust region is smaller than steepest descent update
     VectorValues x_d = VectorValues::SameStructure(dx_u);
-    x_d.vector() = dx_u.vector() * sqrt(DeltaSq / x_u_norm_sq);
-    if(verbose) cout << "In steepest descent region with fraction " << sqrt(DeltaSq / x_u_norm_sq) << " of steepest descent magnitude" << endl;
+    x_d.vector() = dx_u.vector() * std::sqrt(DeltaSq / x_u_norm_sq);
+    if(verbose) cout << "In steepest descent region with fraction " << std::sqrt(DeltaSq / x_u_norm_sq) << " of steepest descent magnitude" << endl;
     return x_d;
   } else if(DeltaSq < x_n_norm_sq) {
     // Trust region boundary is between steepest descent point and Newton's method point
@@ -59,7 +60,7 @@ VectorValues DoglegOptimizerImpl::ComputeBlend(double Delta, const VectorValues&
   const double a = uu - 2.*un + nn;
   const double b = 2. * (un - uu);
   const double c = uu - Delta*Delta;
-  double sqrt_b_m4ac = sqrt(b*b - 4*a*c);
+  double sqrt_b_m4ac = std::sqrt(b*b - 4*a*c);
 
   // Compute blending parameter
   double tau1 = (-b + sqrt_b_m4ac) / (2.*a);
diff --git a/gtsam/nonlinear/ISAM2-impl.cpp b/gtsam/nonlinear/ISAM2-impl.cpp
index 8fe117e34..e8847e5c0 100644
--- a/gtsam/nonlinear/ISAM2-impl.cpp
+++ b/gtsam/nonlinear/ISAM2-impl.cpp
@@ -15,6 +15,7 @@
  * @author  Michael Kaess, Richard Roberts
  */
 
+#include <functional>
 #include <boost/range/adaptors.hpp>
 #include <boost/range/algorithm.hpp>
 #include <gtsam/nonlinear/ISAM2-impl.h>
@@ -149,7 +150,7 @@ void ISAM2::Impl::ExpmapMasked(Values& values, const Permuted<VectorValues>& del
         cout << "       " << keyFormatter(key_value->key) << " (j = " << var << "), delta = " << delta[var].transpose() << endl;
     }
     assert(delta[var].size() == (int)key_value->value.dim());
-    assert(delta[var].unaryExpr(&isfinite<double>).all());
+    assert(delta[var].unaryExpr(ptr_fun(isfinite<double>)).all());
     if(mask[var]) {
       Value* retracted = key_value->value.retract_(delta[var]);
       key_value->value = *retracted;
@@ -305,7 +306,7 @@ size_t ISAM2::Impl::UpdateDelta(const boost::shared_ptr<ISAM2Clique>& root, std:
 
 #ifndef NDEBUG
     for(size_t j=0; j<delta.container().size(); ++j)
-      assert(delta.container()[j].unaryExpr(&isfinite<double>).all());
+      assert(delta.container()[j].unaryExpr(ptr_fun(isfinite<double>)).all());
 #endif
   }
 
diff --git a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
index 1b19cfe75..b9523b538 100644
--- a/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
+++ b/gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
@@ -16,6 +16,8 @@
  * @created Feb 26, 2012
  */
 
+#include <cmath>
+
 #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
 
 #include <gtsam/base/cholesky.h> // For NegativeMatrixException
@@ -48,7 +50,7 @@ void LevenbergMarquardtOptimizer::iterate() {
     // TODO: replace this dampening with a backsubstitution approach
     GaussianFactorGraph dampedSystem(*linear);
     {
-      double sigma = 1.0 / sqrt(state_.lambda);
+      double sigma = 1.0 / std::sqrt(state_.lambda);
       dampedSystem.reserve(dampedSystem.size() + dimensions_.size());
       // for each of the variables, add a prior
       for(Index j=0; j<dimensions_.size(); ++j) {
diff --git a/gtsam/slam/pose2SLAM.cpp b/gtsam/slam/pose2SLAM.cpp
index dd80b821b..1a34904fc 100644
--- a/gtsam/slam/pose2SLAM.cpp
+++ b/gtsam/slam/pose2SLAM.cpp
@@ -27,7 +27,7 @@ namespace pose2SLAM {
     Values x;
     double theta = 0, dtheta = 2 * M_PI / n;
     for (size_t i = 0; i < n; i++, theta += dtheta)
-      x.insert(PoseKey(i), Pose2(cos(theta), sin(theta), M_PI_2 + theta));
+      x.insert(PoseKey(i), Pose2(cos(theta), sin(theta), (M_PI/2.0) + theta));
     return x;
   }