mirror of
https://github.com/holub/mame
synced 2025-10-08 01:28:00 +03:00
2720512e31
Rewrote mat_cr_t to include data as well. Fixed some bitrot in other parts. Simplified solver creation. (nw)
337 lines
8.0 KiB
C++
337 lines
8.0 KiB
C++
// license:GPL-2.0+
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// copyright-holders:Couriersud
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/*
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* nld_ms_direct.h
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*
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*
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* Sherman-Morrison Solver
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*
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* Computes the updated inverse of A given that the change in A is
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*
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* A <- A + (u x v) u,v vectors
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*
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* In this specific implementation, u is a unit vector specifying the row which
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* changed. Thus v contains the changed column.
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*
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* Than z = A^-1 u , w = transpose(A^-1) v , lambda = v z
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*
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* A^-1 <- 1.0 / (1.0 + lambda) * (z x w)
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*
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* The approach is iterative and applied for each row changed.
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*
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* The performance for a typical circuit like kidniki compared to Gaussian
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* elimination is poor:
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*
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* a) The code needs to be run for each row change.
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* b) The inverse of A typically is fully occupied.
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*
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* It may have advantages for circuits with a high number of elements and only
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* few dynamic/active components.
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*
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*/
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#ifndef NLD_MS_SM_H_
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#define NLD_MS_SM_H_
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#include <algorithm>
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#include "solver/nld_solver.h"
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#include "solver/nld_matrix_solver.h"
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#include "solver/vector_base.h"
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namespace netlist
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{
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namespace devices
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{
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//#define nl_ext_double _float128 // slow, very slow
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//#define nl_ext_double long double // slightly slower
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#define nl_ext_double nl_double
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template <std::size_t m_N, std::size_t storage_N>
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class matrix_solver_sm_t: public matrix_solver_t
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{
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friend class matrix_solver_t;
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public:
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matrix_solver_sm_t(netlist_t &anetlist, const pstring &name,
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const solver_parameters_t *params, const std::size_t size);
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virtual ~matrix_solver_sm_t();
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virtual void vsetup(analog_net_t::list_t &nets) override;
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virtual void reset() override { matrix_solver_t::reset(); }
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protected:
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virtual unsigned vsolve_non_dynamic(const bool newton_raphson) override;
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unsigned solve_non_dynamic(const bool newton_raphson);
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constexpr std::size_t N() const { return (m_N == 0) ? m_dim : m_N; }
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void LE_invert();
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template <typename T>
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void LE_compute_x(T * RESTRICT x);
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template <typename T1, typename T2>
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inline nl_ext_double &A(const T1 &r, const T2 &c) { return m_A[r][c]; }
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template <typename T1, typename T2>
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inline nl_ext_double &W(const T1 &r, const T2 &c) { return m_W[r][c]; }
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template <typename T1, typename T2>
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inline nl_ext_double &Ainv(const T1 &r, const T2 &c) { return m_Ainv[r][c]; }
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template <typename T1>
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inline nl_ext_double &RHS(const T1 &r) { return m_RHS[r]; }
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template <typename T1, typename T2>
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inline nl_ext_double &lA(const T1 &r, const T2 &c) { return m_lA[r][c]; }
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template <typename T1, typename T2>
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inline nl_ext_double &lAinv(const T1 &r, const T2 &c) { return m_lAinv[r][c]; }
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nl_double m_last_RHS[storage_N]; // right hand side - contains currents
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private:
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static const std::size_t m_pitch = ((( storage_N) + 7) / 8) * 8;
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nl_ext_double m_A[storage_N][m_pitch];
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nl_ext_double m_Ainv[storage_N][m_pitch];
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nl_ext_double m_W[storage_N][m_pitch];
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nl_ext_double m_RHS[storage_N]; // right hand side - contains currents
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nl_ext_double m_lA[storage_N][m_pitch];
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nl_ext_double m_lAinv[storage_N][m_pitch];
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//nl_ext_double m_RHSx[storage_N];
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const std::size_t m_dim;
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};
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// ----------------------------------------------------------------------------------------
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// matrix_solver_direct
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// ----------------------------------------------------------------------------------------
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template <std::size_t m_N, std::size_t storage_N>
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matrix_solver_sm_t<m_N, storage_N>::~matrix_solver_sm_t()
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{
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#if (NL_USE_DYNAMIC_ALLOCATION)
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plib::pfree_array(m_A);
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#endif
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}
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template <std::size_t m_N, std::size_t storage_N>
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void matrix_solver_sm_t<m_N, storage_N>::vsetup(analog_net_t::list_t &nets)
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{
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matrix_solver_t::setup_base(nets);
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netlist().save(*this, m_last_RHS, "m_last_RHS");
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for (unsigned k = 0; k < N(); k++)
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netlist().save(*this, RHS(k), plib::pfmt("RHS.{1}")(k));
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}
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template <std::size_t m_N, std::size_t storage_N>
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void matrix_solver_sm_t<m_N, storage_N>::LE_invert()
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{
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const std::size_t kN = N();
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for (std::size_t i = 0; i < kN; i++)
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{
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for (std::size_t j = 0; j < kN; j++)
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{
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W(i,j) = lA(i,j) = A(i,j);
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Ainv(i,j) = 0.0;
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}
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Ainv(i,i) = 1.0;
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}
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/* down */
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for (std::size_t i = 0; i < kN; i++)
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{
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/* FIXME: Singular matrix? */
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const nl_double f = 1.0 / W(i,i);
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const auto * RESTRICT const p = m_terms[i]->m_nzrd.data();
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const std::size_t e = m_terms[i]->m_nzrd.size();
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/* Eliminate column i from row j */
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const auto * RESTRICT const pb = m_terms[i]->m_nzbd.data();
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const std::size_t eb = m_terms[i]->m_nzbd.size();
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for (std::size_t jb = 0; jb < eb; jb++)
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{
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const unsigned j = pb[jb];
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const nl_double f1 = - W(j,i) * f;
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if (f1 != 0.0)
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{
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for (std::size_t k = 0; k < e; k++)
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W(j,p[k]) += W(i,p[k]) * f1;
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for (std::size_t k = 0; k <= i; k ++)
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Ainv(j,k) += Ainv(i,k) * f1;
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}
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}
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}
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/* up */
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for (std::size_t i = kN; i-- > 0; )
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{
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/* FIXME: Singular matrix? */
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const nl_double f = 1.0 / W(i,i);
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for (std::size_t j = i; j-- > 0; )
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{
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const nl_double f1 = - W(j,i) * f;
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if (f1 != 0.0)
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{
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for (std::size_t k = i; k < kN; k++)
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W(j,k) += W(i,k) * f1;
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for (std::size_t k = 0; k < kN; k++)
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Ainv(j,k) += Ainv(i,k) * f1;
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}
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}
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for (std::size_t k = 0; k < kN; k++)
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{
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Ainv(i,k) *= f;
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lAinv(i,k) = Ainv(i,k);
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}
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}
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}
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template <std::size_t m_N, std::size_t storage_N>
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template <typename T>
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void matrix_solver_sm_t<m_N, storage_N>::LE_compute_x(
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T * RESTRICT x)
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{
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const std::size_t kN = N();
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for (std::size_t i=0; i<kN; i++)
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x[i] = 0.0;
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for (std::size_t k=0; k<kN; k++)
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{
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const nl_double f = RHS(k);
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for (std::size_t i=0; i<kN; i++)
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x[i] += Ainv(i,k) * f;
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}
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}
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template <std::size_t m_N, std::size_t storage_N>
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unsigned matrix_solver_sm_t<m_N, storage_N>::solve_non_dynamic(const bool newton_raphson)
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{
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static const bool incremental = true;
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static unsigned cnt = 0;
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const auto iN = N();
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nl_double new_V[storage_N]; // = { 0.0 };
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if (0 || ((cnt % 200) == 0))
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{
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/* complete calculation */
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this->LE_invert();
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}
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else
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{
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if (!incremental)
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{
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for (unsigned row = 0; row < iN; row ++)
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for (unsigned k = 0; k < iN; k++)
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Ainv(row,k) = lAinv(row, k);
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}
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for (unsigned row = 0; row < iN; row ++)
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{
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nl_double v[m_pitch] = {0};
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unsigned cols[m_pitch];
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unsigned colcount = 0;
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auto &nz = m_terms[row]->m_nz;
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for (auto & col : nz)
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{
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v[col] = A(row,col) - lA(row,col);
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if (incremental)
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lA(row,col) = A(row,col);
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if (v[col] != 0.0)
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cols[colcount++] = col;
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}
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if (colcount > 0)
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{
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nl_double lamba = 0.0;
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nl_double w[m_pitch] = {0};
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nl_double z[m_pitch];
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/* compute w and lamba */
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for (unsigned i = 0; i < iN; i++)
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z[i] = Ainv(i, row); /* u is row'th column */
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for (unsigned j = 0; j < colcount; j++)
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lamba += v[cols[j]] * z[cols[j]];
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for (unsigned j=0; j<colcount; j++)
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{
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auto col = cols[j];
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auto f = v[col];
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for (unsigned k = 0; k < iN; k++)
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w[k] += Ainv(col,k) * f; /* Transpose(Ainv) * v */
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}
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lamba = -1.0 / (1.0 + lamba);
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for (unsigned i=0; i<iN; i++)
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{
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const nl_double f = lamba * z[i];
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if (f != 0.0)
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for (unsigned k = 0; k < iN; k++)
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Ainv(i,k) += f * w[k];
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}
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}
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}
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}
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cnt++;
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this->LE_compute_x(new_V);
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if (newton_raphson)
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{
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nl_double err = delta(new_V);
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store(new_V);
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return (err > this->m_params.m_accuracy) ? 2 : 1;
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}
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else
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{
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store(new_V);
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return 1;
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}
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}
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template <std::size_t m_N, std::size_t storage_N>
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inline unsigned matrix_solver_sm_t<m_N, storage_N>::vsolve_non_dynamic(const bool newton_raphson)
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{
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build_LE_A<matrix_solver_sm_t>();
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build_LE_RHS<matrix_solver_sm_t>();
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for (std::size_t i=0, iN=N(); i < iN; i++)
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m_last_RHS[i] = RHS(i);
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this->m_stat_calculations++;
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return this->solve_non_dynamic(newton_raphson);
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}
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template <std::size_t m_N, std::size_t storage_N>
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matrix_solver_sm_t<m_N, storage_N>::matrix_solver_sm_t(netlist_t &anetlist, const pstring &name,
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const solver_parameters_t *params, const std::size_t size)
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: matrix_solver_t(anetlist, name, NOSORT, params)
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, m_dim(size)
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{
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for (std::size_t k = 0; k < N(); k++)
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{
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m_last_RHS[k] = 0.0;
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}
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}
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} //namespace devices
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} // namespace netlist
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#endif /* NLD_MS_DIRECT_H_ */
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