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MoochoPack_CheckDecompositionFromPy_Step.cpp
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41 
42 #include <typeinfo>
43 #include <limits>
44 
50 
51 namespace MoochoPack {
52 
54  const new_decomp_strategy_ptr_t &new_decomp_strategy
55  ,value_type max_decomposition_cond_change_frac
56  )
57  :new_decomp_strategy_(new_decomp_strategy)
58  ,max_decomposition_cond_change_frac_( max_decomposition_cond_change_frac )
59  ,max_cond_( 0.01 / std::numeric_limits<value_type>::epsilon() )
60 {
61  reset();
62 }
63 
66 }
67 
68 // Overridden
69 
71  Algorithm& _algo, poss_type step_poss, IterationPack::EDoStepType type, poss_type assoc_step_poss
72  )
73 {
74  NLPAlgo &algo = rsqp_algo(_algo);
75  NLPAlgoState &s = algo.rsqp_state();
76  const Range1D equ_decomp = s.equ_decomp();
77  EJournalOutputLevel olevel = algo.algo_cntr().journal_output_level();
78  std::ostream &out = algo.track().journal_out();
79 
80  // print step header.
81  if( static_cast<int>(olevel) >= static_cast<int>(PRINT_ALGORITHM_STEPS) ) {
83  print_algorithm_step( algo, step_poss, type, assoc_step_poss, out );
84  }
85 
86  bool select_new_decomposition = false;
87 
88  const value_type
90  beta = s.py().get_k(0).norm_inf() / (s.c().get_k(0).sub_view(equ_decomp)->norm_inf()+small_num);
91 
92  if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS ) {
93  out << "\nbeta = ||py||/||c|| = " << beta << std::endl;
94  }
95 
96  // Check to see if a new basis was selected or not
97  IterQuantityAccess<index_type>
98  &num_basis_iq = s.num_basis();
99  if( num_basis_iq.updated_k(0) ) {
100  if( (int)olevel >= (int)PRINT_ALGORITHM_STEPS )
101  out << "\nnum_basis_k was updated so the basis changed so we will skip this check\n"
102  << " reset min ||py||/||c|| to current value + 1\n";
103  beta_min_ = beta + 1.0;
104  return true;
105  }
106 
107  if( beta + 1.0 < beta_min_ ) {
108  beta_min_ = beta + 1.0;
109  }
110  else {
111  if( (beta + 1.0)/ beta_min_ > max_decomposition_cond_change_frac() ) {
112  if( (int)olevel >= (int)PRINT_BASIC_ALGORITHM_INFO ) {
113  out << "Select a new decomposition"
114  << " (k = " << algo.state().k() << ")\n"
115  << "beta_change = ( ||py||/||c|| = " << beta
116  << " ) / ( min ||py||/||c|| = " << beta_min_ << " )\n"
117  << "beta_change = " << (beta/beta_min_) << " > max_decomposition_cond_change_frac = "
118  << max_decomposition_cond_change_frac() << std::endl;
119  }
120  select_new_decomposition = true;
121  }
122  }
123  if( !select_new_decomposition && beta > max_cond() ) {
124  if( (int)olevel >= (int)PRINT_BASIC_ALGORITHM_INFO ) {
125  out << "\nConditioning of decomposition matrix is > " << beta
126  << " > max_cond = " << max_cond() << std::endl
127  << "Selecting a new decomposition ... "
128  << " (k = " << algo.state().k() << ")\n";
129  }
130  select_new_decomposition = true;
131  }
132 
133  if(select_new_decomposition) {
134  reset();
135  return new_decomp_strategy().new_decomposition(algo,step_poss,type,assoc_step_poss);
136  }
137 
138  return true;
139 }
140 
142  const Algorithm& algo, poss_type step_poss
143  ,IterationPack::EDoStepType type, poss_type assoc_step_poss
144  ,std::ostream& out, const std::string& L ) const
145 {
146  out
147  << L << "default: beta_min = inf\n"
148  << L << " max_decomposition_cond_change_frac = " << max_decomposition_cond_change_frac() << std::endl
149  << L << " max_cond = 0.01 * mach_eps\n"
150  << L << "beta = norm_inf(py_k) / (norm_inf(c_k(equ_decomp))+small_number)\n"
151  << L << "select_new_decomposition = false\n"
152  << L << "if num_basis_k is updated then\n"
153  << L << " beta_min = beta + 1\n"
154  << L << "end\n"
155  << L << "if beta + 1 < beta_min then\n"
156  << L << " beta_min = beta + 1\n"
157  << L << "else\n"
158  << L << " if (beta + 1) / beta_min > max_decomposition_cond_change_frac then\n"
159  << L << " select_new_decomposition = true\n"
160  << L << " end\n"
161  << L << "end\n"
162  << L << "if beta > max_cond then\n"
163  << L << " select_new_decomposition = true\n"
164  << L << "end\n"
165  << L << "if select_new_decomposition == true then\n"
166  << L << " new decomposition selection : " << typeName(new_decomp_strategy()) << std::endl
167  ;
168  new_decomp_strategy().print_new_decomposition(
169  rsqp_algo(algo),step_poss,type,assoc_step_poss,out, L + " " );
170  out
171  << L << " end new decomposition selection\n"
172  << L << "end\n"
173  ;
174 }
175 
176 } // end namespace MoochoPack
std::string typeName(const T &t)
void reset()
Call the reset initialization of all defaults.
rSQP Algorithm control class.
bool do_step(Algorithm &algo, poss_type step_poss, IterationPack::EDoStepType type, poss_type assoc_step_poss)
void print_step(const Algorithm &algo, poss_type step_poss, IterationPack::EDoStepType type, poss_type assoc_step_poss, std::ostream &out, const std::string &leading_str) const
virtual std::ostream & journal_out() const
Return a reference to a std::ostream to be used to output debug information and the like...
EJournalOutputLevel
enum for journal output.
Reduced space SQP state encapsulation interface.
std::ostream * out
void print_algorithm_step(const Algorithm &algo, Algorithm::poss_type step_poss, EDoStepType type, Algorithm::poss_type assoc_step_poss, std::ostream &out)
Prints to 'out' the algorithm step.
AlgorithmTracker & track()
AlgorithmState & state()
AbstractLinAlgPack::value_type value_type
Acts as the central hub for an iterative algorithm.
NLPAlgoState & rsqp_state()
<<std aggr>="">> members for algo_cntr
NLPAlgo & rsqp_algo(Algorithm &algo)
Convert from a Algorithm to a NLPAlgo.