Luksan vlcek1 and gradient utils #69
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bluescarni
requested changes
Apr 7, 2017
| namespace pagmo | ||
| { | ||
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| /// Test problem from the Luksan and Vlcek |
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Maybe just from instead of from the.
| * | ||
| * @param dim the problem dimensions. | ||
| * | ||
| * @throw std::invalid_argument if \p dim is < 3 |
| #define PAGMO_PROBLEM_LUKSANVLCECK_HPP | ||
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| #include <cassert> | ||
| #include <exception> |
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I think this header is not used (for std::invalid_argument you need stdexcept, which is included below).
| #define PAGMO_PROBLEM_LUKSANVLCECK_HPP | ||
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| #include <cassert> | ||
| #include <exception> |
| * | ||
| * It returns the box-bounds for this UDP. | ||
| * | ||
| * @return the lower and upper bounds for each of the decision vector components |
pygmo/docstrings.cpp
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| @@ -2805,6 +2838,124 @@ Complexity is :math:`\mathcal{O}(MN)` where :math:`M` is the number of objective | |||
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| std::string estimate_sparsity_docstring() | |||
| { | |||
| return R"(estimate_sparsity_docstring(callable, x, dx = 1e-8) | |||
pygmo/docstrings.cpp
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| std::string estimate_gradient_docstring() | ||
| { | ||
| return R"(estimate_gradient_docstring(callable, x, dx = 1e-8) |
pygmo/docstrings.cpp
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| std::string estimate_gradient_h_docstring() | ||
| { | ||
| return R"(estimate_gradient_docstring(callable, x, dx = 1e-8) |
| * \end{array} | ||
| * \f] | ||
| * | ||
| * See: Lukšan, L., and Jan Vlcek. "Sparse and partially separable test problems for unconstrained and equality |
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I would check if the "š" character survives intact in the doxygen -> sphinx transition. If it does, I'd use it consistently (elsewhere a normal "s" is used).
pygmo/docstrings.cpp
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| +4x_{k+1}-x_k\exp(x_k-x_{k+1})-3 \ge LB, \forall k=1..n-2 \\ | ||
| \end{array} | ||
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| See: Lukšan, L., and Jan Vlcek. "Sparse and partially separable test problems for unconstrained and equality |
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Same here regarding the "š" character. We should check whether it shows up correctly in the final sphinx docs and use it consistently.
bluescarni
approved these changes
Apr 7, 2017
darioizzo
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Apr 8, 2017
bluescarni
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Apr 8, 2017
[skip ci] forgotten from #69
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The first gradientfull scalable problem in pagmo. And some numerical derivation utils