Eigen3.3 support

.travis.yml

@@ -16,36 +16,57 @@ env:
   global:
     - CI_HOME=`pwd`
   matrix:
+    # Test python3
     - LIBCMAES=ON NLOPT=ON TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python3
-    - LIBCMAES=ON NLOPT=ON TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2
-    - LIBCMAES=ON NLOPT=ON TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2
-    - LIBCMAES=ON NLOPT=OFF TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2
-    - LIBCMAES=ON NLOPT=OFF TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2
-    - LIBCMAES=OFF NLOPT=ON TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2
-    - LIBCMAES=OFF NLOPT=ON TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2
-    - LIBCMAES=OFF NLOPT=OFF TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2
-    - LIBCMAES=OFF NLOPT=OFF TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2
-    - LIBCMAES=ON NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=OFF PYTHON=python2
-    - LIBCMAES=OFF NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=OFF PYTHON=python2
-    - LIBCMAES=ON NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=OFF PYTHON=python2
-    - LIBCMAES=OFF NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=OFF PYTHON=python2
-    - LIBCMAES=ON NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=ON PYTHON=python2
-    - LIBCMAES=OFF NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=ON PYTHON=python2
-    - LIBCMAES=ON NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=ON PYTHON=python2
-    - LIBCMAES=OFF NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=ON PYTHON=python2
+    # Test LAPACK/BLAS
+    - LIBCMAES=ON NLOPT=ON TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=ON LAPACKE=--lapacke_blas
+    # Eigen3.3
+    - LIBCMAES=ON NLOPT=ON TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=ON NLOPT=ON TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=ON NLOPT=OFF TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=ON NLOPT=OFF TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=OFF NLOPT=ON TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=OFF NLOPT=ON TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=OFF NLOPT=OFF TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=OFF NLOPT=OFF TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=ON NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=OFF NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=ON NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=OFF NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=OFF PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=ON NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=ON PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=OFF NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=ON PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=ON NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=ON PYTHON=python2 EIGEN33=ON
+    - LIBCMAES=OFF NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=ON PYTHON=python2 EIGEN33=ON
+    # Eigen3
+    - LIBCMAES=ON NLOPT=ON TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=ON NLOPT=ON TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=ON NLOPT=OFF TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=ON NLOPT=OFF TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=OFF NLOPT=ON TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=OFF NLOPT=ON TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=OFF NLOPT=OFF TBB=ON EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=OFF NLOPT=OFF TBB=OFF EXPERIMENTAL=OFF SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=ON NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=OFF NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=ON NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=OFF NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=OFF PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=ON NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=ON PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=OFF NLOPT=OFF TBB=OFF EXPERIMENTAL=ON SFERES=ON PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=ON NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=ON PYTHON=python2 EIGEN33=OFF
+    - LIBCMAES=OFF NLOPT=OFF TBB=ON EXPERIMENTAL=ON SFERES=ON PYTHON=python2 EIGEN33=OFF
 
 addons:
   apt:
     packages:
     - libboost1.55-all-dev
-    - libeigen3-dev
     - python
     - python3
 
 before_install:
   - sudo sed -i -e 's/^Defaults\tsecure_path.*$//' /etc/sudoers
 
 install:
+  - if [ "$EIGEN33" = "ON" ]; then 'ci/install_eigen3_3.sh' ; else 'ci/install_eigen3.sh' ; fi
   - if [ "$LIBCMAES" = "ON" ]; then 'ci/install_libcmaes.sh' ; fi
   - if [ "$NLOPT" = "ON" ]; then 'ci/install_nlopt.sh' ; fi
   - if [ "$TBB" = "ON" ]; then 'ci/install_tbb.sh' ; fi
@@ -54,5 +75,5 @@ install:
 
 # Change this to your needs
 script:
-  - if [ "$SFERES" = "OFF" ]; then $PYTHON ./waf configure ; else $PYTHON ./waf configure --sferes=$CI_HOME/sferes2 ; fi
+  - if [ "$SFERES" = "OFF" ]; then $PYTHON ./waf configure $LAPACKE ; else $PYTHON ./waf configure --sferes=$CI_HOME/sferes2 ; fi
   - if [ "$EXPERIMENTAL" = "OFF" ]; then $PYTHON ./waf --tests --alltests -v ; else $PYTHON ./waf --experimental ; fi

ci/install_eigen3.sh

@@ -0,0 +1,2 @@
+sudo apt-get -qq update
+sudo apt-get -qq --yes --force-yes install libeigen3-dev

ci/install_eigen3_3.sh

@@ -0,0 +1,10 @@
+sudo apt-get -qq update
+sudo apt-get -qq --yes --force-yes install libblas-dev liblapacke liblapacke-dev
+cd && hg clone https://bitbucket.org/eigen/eigen
+cd eigen
+hg up 3.3
+mkdir build && cd build
+cmake ..
+sudo make install
+sudo ldconfig
+cd $CI_HOME

docs/tutorials/advanced_example.rst

@@ -49,8 +49,8 @@ The basic layout of your ``main.cpp`` file should look like this:
 
     template <typename Params>
     struct eval_func {
-      static constexpr int dim_in = sample_dimensions;
-      static constexpr int dim_out = output_dimensions;
+      BO_PARAM(size_t, dim_in, sample_dimensions);
+      BO_PARAM(size_t, dim_out, output_dimensions);
       // Here we define the evaluation function
     };
 

docs/tutorials/basic_example.rst

@@ -54,8 +54,8 @@ Then, we have to define the evaluation function for the optimizer to call:
    :linenos:
    :lines: 98-112
 
-It is required that the evaluation struct has the static members ``dim_in`` and ``dim_out``, specifying the input and output dimensions.
-Also, it should have the ``operator()`` expecting a ``const Eigen::VectorXd&`` of size ``dim_in``, and return another one, of size ``dim_out``.
+It is required that the evaluation struct has the static function members ``dim_in()`` and ``dim_out()``, specifying the input and output dimensions.
+Also, it should have the ``operator()`` expecting a ``const Eigen::VectorXd&`` of size ``dim_in()``, and return another one, of size ``dim_out()``.
 
 With this, we can declare the main function:
 

docs/tutorials/compilation.rst

@@ -33,11 +33,20 @@ Optional but highly recommended
     sudo make
     sudo cp *.a /usr/lib
 
+  In addition, you should be careful to configure **libcmaes** to use the same Eigen3 version as what you intend to use with Limbo (configuring with Makefiles)::
+
+    ./configure --with-eigen3-include=YOUR_DESIRED_DIR/include/eigen3
+
+  or (configure with CMake)::
+
+    cmake -DEIGEN3_INCLUDE_DIR=YOUR_DESIRED_DIR/include/eigen3 ..
+
 * `Intel TBB <https://www.threadingbuildingblocks.org>`_ is not mandatory, but highly recommended; TBB is used in Limbo to take advantage of multicore architectures.
 
 Optional
 +++++++++++++
 * `Intel MKL <https://software.intel.com/en-us/intel-mkl>`_ is supported as backend for Eigen. In our experience, it provided best results when compiling with Intel's Compiler (ICC)
+* `LAPACKE/BLAS <http://www.netlib.org/lapack/lapacke.html>`_ is supported as a backend for Eigen (`version>=3.3 <https://eigen.tuxfamily.org/dox/TopicUsingBlasLapack.html>`_). In our experience, it gives high speed-ups with **big** matrices (i.e., more than 1200 dimensions) and hurts a bit the performance with **small** matrices (i.e., less than 800 dimensions). You can enable LAPACKE/BLAS by using the ``--lapacke_blas`` option (if you have Eigen3.3 or later).
 * `Sferes2 <https://github.com/sferes2/sferes2>`_ if you plan to use the multi-objective bayesian optimization algorithms (experimental).
 
 Compilation

src/benchmarks/bayesopt/hp_opt.cpp

@@ -54,7 +54,7 @@ void benchmark(const bopt_params& par, const std::string& name)
 {
     auto t1 = std::chrono::steady_clock::now();
     Benchmark<Function> benchmark(par);
-    vectord result(Function::dim_in);
+    vectord result(Function::dim_in());
     benchmark.optimize(result);
     auto time_running = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - t1).count();
     std::cout.precision(17);

src/benchmarks/bayesopt/simple.cpp

@@ -54,7 +54,7 @@ void benchmark(const bopt_params& par, const std::string& name)
 {
     auto t1 = std::chrono::steady_clock::now();
     Benchmark<Function> benchmark(par);
-    vectord result(Function::dim_in);
+    vectord result(Function::dim_in());
     benchmark.optimize(result);
     auto time_running = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::steady_clock::now() - t1).count();
     std::cout.precision(17);

src/benchmarks/bayesopt/testfunctions.hpp

@@ -97,8 +97,8 @@ inline vectord t_osz(const vectord& x)
 }
 
 struct Sphere {
-    static constexpr size_t dim_in = 2;
-    static constexpr size_t dim_out = 1;
+    BO_PARAM(size_t, dim_in, 2);
+    BO_PARAM(size_t, dim_out, 1);
 
     double operator()(const vectord& x) const
     {
@@ -117,17 +117,17 @@ struct Sphere {
 };
 
 struct Ellipsoid {
-    static constexpr size_t dim_in = 2;
-    static constexpr size_t dim_out = 1;
+    BO_PARAM(size_t, dim_in, 2);
+    BO_PARAM(size_t, dim_out, 1);
 
     double operator()(const vectord& x) const
     {
         vectord opt(2);
         opt <<= 0.5, 0.5;
         vectord z = t_osz(x - opt);
         double r = 0;
-        for (size_t i = 0; i < dim_in; ++i)
-            r += std::pow(10, ((double)i) / (dim_in - 1.0)) * z(i) * z(i) + 1;
+        for (size_t i = 0; i < dim_in(); ++i)
+            r += std::pow(10, ((double)i) / (dim_in() - 1.0)) * z(i) * z(i) + 1;
         return r;
     }
 
@@ -140,30 +140,30 @@ struct Ellipsoid {
 };
 
 struct Rastrigin {
-    static constexpr size_t dim_in = 4;
-    static constexpr size_t dim_out = 1;
+    BO_PARAM(size_t, dim_in, 4);
+    BO_PARAM(size_t, dim_out, 1);
 
     double operator()(const vectord& x) const
     {
-        double f = 10 * dim_in;
-        for (size_t i = 0; i < dim_in; ++i)
+        double f = 10 * dim_in();
+        for (size_t i = 0; i < dim_in(); ++i)
             f += x(i) * x(i) - 10 * cos(2 * M_PI * x(i));
         return f;
     }
 
     matrixd solutions() const
     {
-        matrixd sols(1, dim_in);
-        for (size_t i = 0; i < dim_in; ++i)
+        matrixd sols(1, dim_in());
+        for (size_t i = 0; i < dim_in(); ++i)
             sols(0, i) = 0;
         return sols;
     }
 };
 
 // see : http://www.sfu.ca/~ssurjano/hart3.html
 struct Hartmann3 {
-    static constexpr size_t dim_in = 3;
-    static constexpr size_t dim_out = 1;
+    BO_PARAM(size_t, dim_in, 3);
+    BO_PARAM(size_t, dim_out, 1);
 
     double operator()(const vectord& x) const
     {
@@ -196,8 +196,8 @@ struct Hartmann3 {
 
 // see : http://www.sfu.ca/~ssurjano/hart6.html
 struct Hartmann6 {
-    static constexpr size_t dim_in = 6;
-    static constexpr size_t dim_out = 1;
+    BO_PARAM(size_t, dim_in, 6);
+    BO_PARAM(size_t, dim_out, 1);
 
     double operator()(const vectord& x) const
     {
@@ -234,8 +234,8 @@ struct Hartmann6 {
 // see : http://www.sfu.ca/~ssurjano/goldpr.html
 // (with ln, as suggested in Jones et al.)
 struct GoldsteinPrice {
-    static constexpr size_t dim_in = 2;
-    static constexpr size_t dim_out = 1;
+    BO_PARAM(size_t, dim_in, 2);
+    BO_PARAM(size_t, dim_out, 1);
 
     double operator()(const vectord& xx) const
     {
@@ -256,8 +256,8 @@ struct GoldsteinPrice {
 };
 
 struct BraninNormalized {
-    static constexpr size_t dim_in = 2;
-    static constexpr size_t dim_out = 1;
+    BO_PARAM(size_t, dim_in, 2);
+    BO_PARAM(size_t, dim_out, 1);
 
     double operator()(const vectord& x) const
     {
@@ -277,8 +277,9 @@ struct BraninNormalized {
 };
 
 struct SixHumpCamel {
-    static constexpr size_t dim_in = 2;
-    static constexpr size_t dim_out = 1;
+    BO_PARAM(size_t, dim_in, 2);
+    BO_PARAM(size_t, dim_out, 1);
+
     double operator()(const vectord& x) const
     {
         double x1 = -3 + 6 * x(0);
@@ -308,7 +309,7 @@ struct SixHumpCamel {
 template <typename Function>
 class Benchmark : public bayesopt::ContinuousModel {
 public:
-    Benchmark(bopt_params par) : ContinuousModel(Function::dim_in, par) {}
+    Benchmark(bopt_params par) : ContinuousModel(Function::dim_in(), par) {}
 
     double evaluateSample(const vectord& xin)
     {

src/benchmarks/limbo/hp_opt.cpp

@@ -109,10 +109,10 @@ template <typename Optimizer, typename Function>
 void benchmark(const std::string& name)
 {
     int iters_base = 250;
-    DirectParams::opt_nloptnograd::set_iterations(static_cast<int>(iters_base * Function::dim_in * 0.9));
-    BobyqaParams::opt_nloptnograd::set_iterations(iters_base * Function::dim_in - DirectParams::opt_nloptnograd::iterations());
+    DirectParams::opt_nloptnograd::set_iterations(static_cast<int>(iters_base * Function::dim_in() * 0.9));
+    BobyqaParams::opt_nloptnograd::set_iterations(iters_base * Function::dim_in() - DirectParams::opt_nloptnograd::iterations());
 
-    BobyqaParams_HP::opt_nloptnograd::set_iterations(10 * Function::dim_in * Function::dim_in);
+    BobyqaParams_HP::opt_nloptnograd::set_iterations(10 * Function::dim_in() * Function::dim_in());
 
     auto t1 = std::chrono::steady_clock::now();
     Optimizer opt;

src/benchmarks/limbo/simple.cpp

@@ -99,8 +99,8 @@ template <typename Optimizer, typename Function>
 void benchmark(const std::string& name)
 {
     int iters_base = 250;
-    DirectParams::opt_nloptnograd::set_iterations(static_cast<int>(iters_base * Function::dim_in * 0.9));
-    BobyqaParams::opt_nloptnograd::set_iterations(iters_base * Function::dim_in - DirectParams::opt_nloptnograd::iterations());
+    DirectParams::opt_nloptnograd::set_iterations(static_cast<int>(iters_base * Function::dim_in() * 0.9));
+    BobyqaParams::opt_nloptnograd::set_iterations(iters_base * Function::dim_in() - DirectParams::opt_nloptnograd::iterations());
 
     auto t1 = std::chrono::steady_clock::now();
     Optimizer opt;