Initial import
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139
libLSS/tools/optimization/array_helper.hpp
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139
libLSS/tools/optimization/array_helper.hpp
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/*+
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ARES/HADES/BORG Package -- ./libLSS/tools/optimization/array_helper.hpp
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Copyright (C) 2018-2019 Guilhem Lavaux <guilhem.lavaux@iap.fr>
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Copyright (C) 2009-2020 Jens Jasche <jens.jasche@fysik.su.se>
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Additional contributions from:
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Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
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+*/
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#ifndef __LIBLSS_TOOLS_OPTIMIZATION_ARRAY_HELPER_HPP
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#define __LIBLSS_TOOLS_OPTIMIZATION_ARRAY_HELPER_HPP
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#include <complex>
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#include <boost/tti/has_type.hpp>
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#include <boost/multi_array.hpp>
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#include "libLSS/tools/array_tools.hpp"
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#include "libLSS/tools/fusewrapper.hpp"
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#include "libLSS/tools/uninitialized_type.hpp"
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namespace LibLSS {
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namespace Optimization {
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BOOST_TTI_HAS_TYPE(array_t);
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BOOST_TTI_HAS_TYPE(holder_array_t);
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template<typename T> struct is_complex: public std::false_type {};
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template<typename U> struct is_complex<std::complex<U>> : public std::true_type {};
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template <typename Array1, typename Array2, typename U = typename Array1::array_t::element>
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inline typename std::enable_if<!is_complex<U>::value, double>::type dotprod(Array1 const &a1, Array2 const &a2) {
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return (a1*a2).sum();
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}
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template <typename Array1, typename Array2, typename U = typename Array1::array_t::element>
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inline typename std::enable_if<is_complex<U>::value, double>::type dotprod(Array1 const &a1, Array2 const &a2) {
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auto r = [](auto&& a) { return std::real(a); };
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auto i = [](auto&& a) { return std::imag(a); };
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auto r1 = r(a1);
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auto r2 = r(a2);
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auto i1 = i(a1);
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auto i2 = i(a2);
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return (r1*r2 + i1*i2).sum();
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}
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template <typename Array1, typename Array2>
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inline double dotprod(MPI_Communication* comm, Array1 const &a1, Array2 const &a2) {
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double r = dotprod(a1, a2);
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comm->all_reduce_t(MPI_IN_PLACE, &r, 1, MPI_SUM);
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return r;
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}
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template <typename Array>
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struct array_holder {
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public:
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typedef Array array_t;
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typedef UninitializedArray<array_t> u_array_t;
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typedef std::shared_ptr<u_array_t> holder_array_t;
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holder_array_t holder;
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array_holder() : holder() {}
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array_holder(array_holder<Array> &other) : holder(other.holder) {}
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array_holder(array_holder<Array> &&other)
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: holder(std::move(other.holder)) {}
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array_holder(holder_array_t &&h) : holder(std::move(h)) {}
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array_holder(holder_array_t& h) : holder(h) {}
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array_holder<Array> const &operator=(array_holder<Array> &other) {
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holder = other.holder;
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return *this;
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}
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array_holder<Array> const &operator=(array_holder<Array> &&other) {
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holder = std::move(other.holder);
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return *this;
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}
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inline array_t &get() { return holder.get()->get_array(); }
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inline array_t const &get() const { return holder.get()->get_array(); }
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inline operator bool() const {
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return holder.operator bool();
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}
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inline auto operator*() -> decltype(fwrap(get())) {
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return fwrap(get());
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}
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inline auto operator*() const -> decltype(fwrap(get())) {
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return fwrap(get());
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}
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};
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template <typename T, typename = void>
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struct is_holder : std::false_type {};
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template <typename T>
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struct is_holder<
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T, typename std::enable_if<
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has_type_array_t<T>::value &&
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has_type_holder_array_t<T>::value>::type> : std::true_type {};
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template <typename T, size_t N>
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struct BoostArrayAllocator {
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public:
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typedef boost::multi_array_ref<T, N> array_t;
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typedef array_holder<array_t> holder_t;
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typedef typename holder_t::u_array_t new_array_t;
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typedef decltype(fwrap(*(array_t *)0)) wrap_t;
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inline auto wrapper(array_t const &a) const -> decltype(fwrap(a)) {
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return fwrap(a);
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}
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inline auto wrapper(array_t &a) const -> decltype(fwrap(a)) {
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return fwrap(a);
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}
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inline holder_t new_like(holder_t const &a) { return new_like(a.get()); }
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template<typename ArrayLike>
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inline holder_t new_like(ArrayLike const &a) {
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return holder_t(std::unique_ptr<new_array_t>(
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new new_array_t(LibLSS::array::make_extent_from(a)))
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);
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}
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};
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} // namespace Optimization
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} // namespace LibLSS
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// ARES TAG: num_authors = 1
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// ARES TAG: name(0) = Guilhem Lavaux
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// ARES TAG: email(0) = guilhem.lavaux@iap.fr
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// ARES TAG: year(0) = 2018-2019
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#endif
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265
libLSS/tools/optimization/cg.hpp
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265
libLSS/tools/optimization/cg.hpp
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/*+
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ARES/HADES/BORG Package -- ./libLSS/tools/optimization/cg.hpp
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Copyright (C) 2014-2020 Guilhem Lavaux <guilhem.lavaux@iap.fr>
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Copyright (C) 2009-2020 Jens Jasche <jens.jasche@fysik.su.se>
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Additional contributions from:
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Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
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+*/
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#ifndef __LIBLSS_TOOLS_OPTIMIZATION_CG_HPP
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#define __LIBLSS_TOOLS_OPTIMIZATION_CG_HPP
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#include <algorithm>
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#include <functional>
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#include <boost/multi_array.hpp>
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#include <boost/bind.hpp>
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#include "libLSS/tools/console.hpp"
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#include "libLSS/tools/array_tools.hpp"
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#include "libLSS/tools/fused_array.hpp"
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#include "libLSS/tools/fused_assign.hpp"
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#include "libLSS/tools/optimization/array_helper.hpp"
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namespace LibLSS {
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namespace Optimization {
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namespace details {
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template <typename T>
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T lazy_conj(T const &c) {
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return c;
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}
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template <typename T>
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std::complex<T> lazy_conj(std::complex<T> const &c) {
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return std::conj(c);
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}
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} // namespace details
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template <typename ArrayAllocator>
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struct CG {
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public:
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unsigned int cg_refresh;
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double epsilon;
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unsigned int T;
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typedef typename ArrayAllocator::array_t array_t;
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typedef std::function<void(array_t& out, array_t const& in)> Matrix_function_t;
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typedef std::function<void(array_t& out, array_t const& in, int s)> Precon_Matrix_function_t;
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public:
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CG(ArrayAllocator alloc_ = ArrayAllocator()):allocator(alloc_) {
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cg_refresh = 1000; // sets refreshing rate for conjugating directions
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epsilon = 1e-9; // sets convergence criterion
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T = 40000000; // sets maximum number of cg steps
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}
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~CG() {}
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void
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run(Matrix_function_t A, array_t const &b,
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array_t &x) {
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ConsoleContext<LOG_VERBOSE> ctx("CG::run");
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Console &cons = Console::instance();
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auto r = allocator.new_like(x);
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auto d = allocator.new_like(x);
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auto q = allocator.new_like(x);
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auto wx = allocator.wrapper(x);
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auto wb = allocator.wrapper(b);
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double dnew = 0.0;
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double dinit = 0.0;
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double dold = 0.0;
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//apply matrix to x vector
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A(q.get(), x);
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//initialize values
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*r = b - *q;
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*d = *r;
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dinit = dotprod(wb,wb);
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dnew = dotprod(*r, *r);
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dold = dnew;
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int t = 0;
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Progress<LOG_INFO_SINGLE> &progress =
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cons.start_progress<LOG_INFO_SINGLE>(
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"applying conjugate gradient", T, 10);
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while ((t < T)) {
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//apply matrix to d vector
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A(q.get(), d.get());
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double dq = dotprod(*d, *q);
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cons.print<LOG_DEBUG>(boost::format("residue is dq=%g ") % dq);
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//now determine alpha
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double alpha = dnew / (dq+1e-40);
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cons.print<LOG_DEBUG>(boost::format("alpha =%g ") % alpha);
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//now update x vector
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wx = wx + alpha * (*d);
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bool exit= false;
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if (t % cg_refresh == 0) {
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A(q.get(), x);
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*r = b -(*q);
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exit=true;
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cons.print<LOG_DEBUG>("Refresh! ");
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} else {
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*r = r.get() - alpha*(*q);
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}
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dold = dnew;
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dnew = dotprod(*r, *r);
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double const ratio = dnew/dinit;
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cons.print<LOG_DEBUG>(boost::format("t=%g residue is dnew=%g / dinit=%g => ratio=%g") % t % dnew % dinit % ratio);
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double beta = dnew / dold;
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cons.print<LOG_DEBUG>(boost::format("beta - 1 =%g ") % (beta-1.));
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*d = *r + beta*(*d);
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t++;
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progress.update(t);
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double const dcheck = dotprod(*r, *r);
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cons.print<LOG_DEBUG>(boost::format("residue is dnew=%g / dcheck=%g / dinit=%g") % dnew % dcheck % dinit);
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if( (dcheck/dinit)<epsilon) { // or ( fabs(beta-1.) < epsilon ))
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A(q.get(), x);
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double const dcheck2 = dotprod(*q - b, *q - b);
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cons.print<LOG_DEBUG>(boost::format("breaking at %g") % (dcheck/dinit));
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cons.print<LOG_DEBUG>(boost::format("breaking at %g??") % (dcheck2/dinit));
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if ((dcheck2/dinit)<epsilon)
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break;
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cons.print<LOG_DEBUG>("no");
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}
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}
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cons.print<LOG_DEBUG>("Done with CG");
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progress.destroy();
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}
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void
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run(Matrix_function_t A, Precon_Matrix_function_t M_inv, typename ArrayAllocator::array_t const&b,
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typename ArrayAllocator::array_t &x) {
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ConsoleContext<LOG_VERBOSE> ctx("CG::run");
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Console &cons = Console::instance();
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auto r = allocator.new_like(x);
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auto d = allocator.new_like(x);
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auto q = allocator.new_like(x);
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auto s = allocator.new_like(x);
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double dnew = 0.0;
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double dinit = 0.0;
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double dold = 0.0;
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//apply matrix to x vector
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A(q.get(), x);
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//initialize values
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*r = b - *q;
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//use preconditioner
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M_inv(d.get(), r.get(),1);
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dinit = dnew = dotprod(*r, *d);
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dold = dnew;
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int t = 0;
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Progress<LOG_INFO_SINGLE> &progress =
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cons.start_progress<LOG_INFO_SINGLE>(
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"applying conjugate gradient", T, 10);
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while ((t < T)) {// and (dinit > epsilon * epsilon)) {
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//apply matrix to d vector
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A(q.get(), d.get());
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double dq = dotprod(*d, *q);
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//now determine alpha
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double alpha = dnew / (dq+1e-40);
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//now update x vector
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fwrap(x) = fwrap(x) + alpha*(*d);
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bool exit=false;
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if (t % cg_refresh == 0) {
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A(q.get(), x);
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*r = b -(*q);
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exit=true;
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} else {
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*r = r.get() - alpha*(*q);
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}
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M_inv(s.get(), r.get(),1);
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dold = dnew;
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dnew = dotprod(*r, *s);
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double beta = dnew / dold;
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*d = *s + beta*(*d);
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t++;
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progress.update(t);
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double const dcheck = dotprod(*r, *r);
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cons.print<LOG_DEBUG>(boost::format("residue is dnew=%g / dcheck=%g / dinit=%g") % dnew % dcheck % dinit);
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if( (dcheck/dinit)<epsilon) { // or ( fabs(beta-1.) < epsilon ))
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cons.print<LOG_DEBUG>(boost::format("breaking at %g") % (dcheck/dinit));
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break;
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}
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}
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progress.destroy();
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}
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private:
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ArrayAllocator allocator;
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};
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} // namespace Optimization
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using Optimization::CG;
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}; // namespace LibLSS
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#endif
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