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/*
* MIT No Attribution
*
* Copyright (C) 2010-2023 Joel Andersson, Joris Gillis, Moritz Diehl, KU Leuven.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy of this
* software and associated documentation files (the "Software"), to deal in the Software
* without restriction, including without limitation the rights to use, copy, modify,
* merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
* permit persons to whom the Software is furnished to do so.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
* INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
* PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
* HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
* OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*
*/
/** \brief Demonstration of sparsity propagation in CasADi
* NOTE: Example is mainly intended for developers of CasADi.
* This example demonstrates how dependencies can be propagated through symbolic expressions,
* a low-level feature useful when determining sparsity patterns. This functionality is mostly
* used internally in CasADi when calculating the sparsity pattern of Jacobians and Hessians.
*
* \author Joel Andersson
* \date 2012
*/
#include "casadi/casadi.hpp"
using namespace casadi;
// Print a (typically 64-bit) unsigned integer
void print_binary(bvec_t v){
for(int k=0; k<bvec_size; ++k){
if(k%4==0) std::cout << " ";
if(k%16==0) std::cout << " ";
if(v & (bvec_t(1)<<k)){
std::cout << 1;
} else {
std::cout << 0;
}
}
std::cout << std::endl;
}
int main(){
// Test both SX and MX
for(int test=0; test<2; ++test){
// Create a simple function
Function f;
if(test==0){
std::cout << "SX:" << std::endl;
SX x = SX::sym("x",3);
SX z = x(0)*x(0)+x(2) + 3;
f = Function("f", {x}, {z});
} else {
std::cout << "MX:" << std::endl;
MX x = MX::sym("x",3);
MX z = x(0)*x(0)+x(2) + 3;
f = Function("f", {x}, {z});
}
// Memory for inputs and outputs
std::vector<bvec_t> f_in(f.nnz_in(0), 0);
std::vector<bvec_t> f_out(f.nnz_out(0), 0);
// Propagate from input to output (forward mode)
std::cout << "forward mode" << std::endl;
// Make sure that the class is able to support the dependency propagation
casadi_assert(f.has_spfwd(), "Forward sparsity propagation not supported");
// Pass seeds
f_in[0] = bvec_t(1) << 0; // seed in direction 0
f_in[1] = bvec_t(1) << 2; // seed in direction 2
f_in[2] = (bvec_t(1) << 4) | (bvec_t(1) << 63); // seed in direction 4 and 63
// Reset sensitivities
f_out[0] = 0;
// Propagate dependencies
f({&f_in.front()}, {&f_out.front()});
// Print the result
print_binary(f_out[0]);
// Propagate from output to input (adjoint/reverse/backward mode)
std::cout << "backward mode" << std::endl;
// Make sure that the class is able to support the dependency propagation
casadi_assert(f.has_sprev(), "Backward sparsity propagation not supported");
// Pass seeds
f_out[0] = (bvec_t(1) << 5) | (bvec_t(1) << 6); // seed in direction 5 and 6
// Reset sensitivities
f_in[0] = 0;
f_in[1] = 0;
f_in[2] = 0;
// Propagate dependencies
f.rev({&f_in.front()}, {&f_out.front()});
// Print the result
print_binary(f_in[0]);
print_binary(f_in[1]);
print_binary(f_in[2]);
}
return 0;
}
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