Luksan vlcek1 and gradient utils

doc/sphinx/docs/cpp/cpp_docs.rst

@@ -80,6 +80,7 @@ Various optimization utilities.
   utils/constrained
   utils/discrepancy
   utils/hypervolume
+  utils/gradient_and_hessians
 
 Miscellanea
 ^^^^^^^^^^^

doc/sphinx/docs/cpp/utils/gradient_and_hessians.rst

@@ -0,0 +1,18 @@
+.. _cpp_gradient_utils:
+
+Utilities for gradient and hessians 
+===================================
+
+A number of utilities to help with problems with gradient and hessians.
+
+--------------------------------------------------------------------------
+
+.. doxygenfunction:: pagmo::estimate_sparsity
+
+--------------------------------------------------------------------------
+
+.. doxygenfunction:: pagmo::estimate_gradient
+
+--------------------------------------------------------------------------
+
+.. doxygenfunction:: pagmo::estimate_gradient_h

doc/sphinx/quickstart.rst

@@ -17,7 +17,7 @@ After following the :ref:`install` you will be able to compile and run your firs
    :language: c++
    :linenos:
 
-Place it into a getting_started.cpp text file and compile it (for eaxmple) with:
+Place it into a getting_started.cpp text file and compile it (for example) with:
 
 .. code-block:: bash
 

include/pagmo/detail/prime_numbers.hpp

@@ -30,7 +30,7 @@ see https://www.gnu.org/licenses/. */
 #define PAGMO_PRIME_NUMBERS_HPP
 
 #include <array>
-#include <exception>
+#include <stdexcept>
 
 #include "../exceptions.hpp"
 

include/pagmo/exceptions.hpp

@@ -35,7 +35,7 @@ see https://www.gnu.org/licenses/. */
  * This header contains exception-related utils used within pagmo.
  */
 
-#include <exception>
+#include <stdexcept>
 #include <stdexcept>
 #include <string>
 #include <type_traits>

include/pagmo/problems/ackley.hpp

@@ -29,7 +29,6 @@ see https://www.gnu.org/licenses/. */
 #ifndef PAGMO_PROBLEM_ACKLEY_HPP
 #define PAGMO_PROBLEM_ACKLEY_HPP
 
-#include <exception>
 #include <iostream>
 #include <stdexcept>
 #include <string>

include/pagmo/problems/luksan_vlcek1.hpp

@@ -0,0 +1,225 @@
+/* Copyright 2017 PaGMO development team
+
+This file is part of the PaGMO library.
+
+The PaGMO library is free software; you can redistribute it and/or modify
+it under the terms of either:
+
+  * the GNU Lesser General Public License as published by the Free
+    Software Foundation; either version 3 of the License, or (at your
+    option) any later version.
+
+or
+
+  * the GNU General Public License as published by the Free Software
+    Foundation; either version 3 of the License, or (at your option) any
+    later version.
+
+or both in parallel, as here.
+
+The PaGMO library is distributed in the hope that it will be useful, but
+WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
+or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
+for more details.
+
+You should have received copies of the GNU General Public License and the
+GNU Lesser General Public License along with the PaGMO library.  If not,
+see https://www.gnu.org/licenses/. */
+
+#ifndef PAGMO_PROBLEM_LUKSANVLCECK_HPP
+#define PAGMO_PROBLEM_LUKSANVLCECK_HPP
+
+#include <cassert>
+#include <iostream>
+#include <stdexcept>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "../detail/constants.hpp"
+#include "../exceptions.hpp"
+#include "../problem.hpp" // needed for cereal registration macro
+#include "../types.hpp"
+
+namespace pagmo
+{
+
+/// Test problem from Luksan and Vlcek
+/**
+ * Implementation of Example 5.1 in the report from Luksan and Vlcek.
+ *
+ * The problem is the Chained Rosenbrock function with trigonometric-exponential
+ * constraints.
+ *
+ * Its formulation in pagmo can be written as:
+ *
+ * \f[
+ * \begin{array}{rl}
+ * \mbox{find:}      & -5 \le \mathbf x_i \le 5, \forall i=1..n\\
+ * \mbox{to minimize: } & \sum_{i=1}^{n-1}\left[100\left(x_i^2-x_{i+1}\right)^2 + \left(x_i-1\right)^2\right]\\
+ * \mbox{subject to:} & 3x_{k+1}^3+2x_{k+2}-5+\sin(x_{k+1}-x_{k+2}})\sin(x_{k+1}+x_{k+2}})
+ * +4x_{k+1}-x_k\exp(x_k-x_{k+1})-3 \le UB, \forall k=1..n-2 \\
+ *                    & 3x_{k+1}^3+2x_{k+2}-5+\sin(x_{k+1}-x_{k+2}})\sin(x_{k+1}+x_{k+2}})
+ * +4x_{k+1}-x_k\exp(x_k-x_{k+1})-3 \ge LB, \forall k=1..n-2 \\
+ * \end{array}
+ * \f]
+ *
+ * See: Luksan, L., and Jan Vlcek. "Sparse and partially separable test problems for unconstrained and equality
+ * constrained optimization." (1999). http://folk.uib.no/ssu029/Pdf_file/Luksan99.ps
+ *
+ */
+struct luksan_vlcek1 {
+    /// Constructor from dimension and bounds
+    /**
+     * Constructs the luksan_vlcek1 problem.
+     *
+     * @param dim the problem dimensions.
+     *
+     * @throw std::invalid_argument if \p dim is < 3.
+     */
+    luksan_vlcek1(unsigned dim = 3u) : m_dim(dim)
+    {
+        if (dim < 3u) {
+            pagmo_throw(std::invalid_argument,
+                        "luksan_vlcek1 must have minimum 3 dimension, " + std::to_string(dim) + " requested");
+        }
+    };
+    /// Fitness computation
+    /**
+     * Computes the fitness for this UDP.
+     *
+     * @param x the decision vector.
+     *
+     * @return the fitness of \p x.
+     */
+    vector_double fitness(const vector_double &x) const
+    {
+        assert(x.size() == m_dim);
+        auto n = x.size();
+        // 1 objective and (n-2) equalities
+        vector_double f(1 + (n - 2), 0.);
+        f[0] = 0.;
+        for (decltype(n) i = 0u; i < n - 1u; ++i) {
+            double a1 = x[i] * x[i] - x[i + 1];
+            double a2 = x[i] - 1.;
+            f[0] += 100. * a1 * a1 + a2 * a2;
+        }
+        for (decltype(n) i = 0u; i < n - 2u; ++i) {
+            f[i + 1] = (3. * std::pow(x[i + 1], 3.) + 2. * x[i + 2] - 5.
+                        + std::sin(x[i + 1] - x[i + 2]) * std::sin(x[i + 1] + x[i + 2]) + 4. * x[i + 1]
+                        - x[i] * std::exp(x[i] - x[i + 1]) - 3.);
+        }
+        return f;
+    }
+    /// Box-bounds
+    /**
+     * It returns the box-bounds for this UDP.
+     *
+     * @return the lower and upper bounds for each of the decision vector components.
+     */
+    std::pair<vector_double, vector_double> get_bounds() const
+    {
+        vector_double lb(m_dim, -5.);
+        vector_double ub(m_dim, 5.);
+        return {lb, ub};
+    }
+    /// Inequality constraint dimension
+    /**
+     *
+     * It returns the number of inequality constraints.
+     *
+     * @return the number of inequality constraints.
+     */
+    vector_double::size_type get_nec() const
+    {
+        return (m_dim - 2);
+    }
+    /// Gradients
+    /**
+     * It returns the fitness gradient.
+     *
+     * The gradient is represented in a sparse form as required by
+     * problem::gradient().
+     *
+     * @param x the decision vector.
+     *
+     * @return the gradient of the fitness function.
+     */
+    vector_double gradient(const vector_double &x) const
+    {
+        assert(x.size() == m_dim);
+        auto n = x.size();
+        vector_double grad(n + 3 * (n - 2), 0.);
+        for (decltype(n) i = 0u; i < n - 1; ++i) {
+            grad[i] += 400. * x[i] * (x[i] * x[i] - x[i + 1]) + 2. * (x[i] - 1.);
+            grad[i + 1] = -200. * (x[i] * x[i] - x[i + 1]);
+        }
+        for (decltype(n) i = 0u; i < n - 2; ++i) {
+            // x[i]
+            grad[n + 3 * i] = -(1. + x[i]) * std::exp(x[i] - x[i + 1]);
+            // x[i+1]
+            grad[n + 1 + 3 * i] = 9. * x[i + 1] * x[i + 1]
+                                  + std::cos(x[i + 1] - x[i + 2]) * std::sin(x[i + 1] + x[i + 2])
+                                  + std::sin(x[i + 1] - x[i + 2]) * std::cos(x[i + 1] + x[i + 2]) + 4.
+                                  + x[i] * std::exp(x[i] - x[i + 1]);
+            // x[i+2]
+            grad[n + 2 + 3 * i] = 2. - std::cos(x[i + 1] - x[i + 2]) * std::sin(x[i + 1] + x[i + 2])
+                                  + std::sin(x[i + 1] - x[i + 2]) * std::cos(x[i + 1] + x[i + 2]);
+        }
+        return grad;
+    }
+    /// Gradients sparsity
+    /**
+     * It returns the gradent sparisty structure for the Luksan Vlcek 1 problem.
+     *
+     * The gradients sparisty is represented in the form required by
+     * problem::gradient_sparsity().
+     *
+     * @return the gradient sparsity structure of the fitness function.
+     */
+    sparsity_pattern gradient_sparsity() const
+    {
+        sparsity_pattern retval;
+        // The part relative to the objective function is dense
+        for (decltype(m_dim) i = 0u; i < m_dim; ++i) {
+            retval.emplace_back(0, i);
+        }
+        // The part relative to the inequality constraints is sparse as each
+        // constraint c_k depends on x_k, x_{k+1} and x_{k+2}
+        for (decltype(m_dim) i = 0u; i < m_dim - 2u; ++i) {
+            retval.emplace_back(i + 1, i);
+            retval.emplace_back(i + 1, i + 1);
+            retval.emplace_back(i + 1, i + 2);
+        }
+        return retval;
+    }
+    /// Problem name
+    /**
+     * @return a string containing the problem name.
+     */
+    std::string get_name() const
+    {
+        return "luksan_vlcek1";
+    }
+    /// Object serialization
+    /**
+     * This method will save/load \p this into the archive \p ar.
+     *
+     * @param ar target archive.
+     *
+     * @throws unspecified any exception thrown by the serialization of the UDP and of primitive types.
+     */
+    template <typename Archive>
+    void serialize(Archive &ar)
+    {
+        ar(m_dim);
+    }
+    /// Problem dimensions.
+    unsigned int m_dim;
+};
+
+} // namespace pagmo
+
+PAGMO_REGISTER_PROBLEM(pagmo::luksan_vlcek1)
+
+#endif