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// Copyright (c) 2016 Frank Fischer <frank-fischer@shadow-soft.de>
//
// This program is free software: you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation, either version 3 of the
// License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful, but
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// Copyright (c) 2016, 2017 Frank Fischer <frank-fischer@shadow-soft.de>
//
// This program is free software: you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation, either version 3 of the
// License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful, but
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self.nxt_y.add(&self.cur_y, &self.nxt_d);
self.nxt_mod = self.master.get_primoptval();
self.sgnorm = self.master.get_dualoptnorm2().sqrt();
self.expected_progress = self.cur_val - self.nxt_mod;
// update multiplier from master solution
for i in 0..self.problem.num_subproblems() {
for m in self.minorants[i].iter_mut() {
m.multiplier = self.master.multiplier(m.index);
}
}
debug!("Model result");
debug!(" cur_val ={}", self.cur_val);
debug!(" nxt_mod ={}", self.nxt_mod);
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self.nxt_y.add(&self.cur_y, &self.nxt_d);
self.nxt_mod = self.master.get_primoptval();
self.sgnorm = self.master.get_dualoptnorm2().sqrt();
self.expected_progress = self.cur_val - self.nxt_mod;
// update multiplier from master solution
for i in 0..self.problem.num_subproblems() {
for m in &mut self.minorants[i] {
m.multiplier = self.master.multiplier(m.index);
}
}
debug!("Model result");
debug!(" cur_val ={}", self.cur_val);
debug!(" nxt_mod ={}", self.nxt_mod);
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if nxt_ub > descent_bnd {
// upper bound will produce null-step
if self.cur_val - nxt_lb > (self.cur_val - self.nxt_mod) * self.params.nullstep_factor.max(0.5) {
warn!("Relative precision of returned objective interval enforces null-step.");
self.iterinfos.push(IterationInfo::UpperBoundNullStep);
}
}
} else {
// lower bound gives already a null step
if self.cur_val - nxt_lb > 0.8 * (self.cur_val - self.nxt_mod) {
// subgradient won't yield much improvement
warn!("Shallow cut (subgradient won't yield much improvement)");
self.iterinfos.push(IterationInfo::ShallowCut);
}
return Ok(Step::Descent);
} else {
self.null_step();
return Ok(Step::Null);
}
}
/**
* Return the bound on the function value that enforces a
* nullstep.
*
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if nxt_ub > descent_bnd {
// upper bound will produce null-step
if self.cur_val - nxt_lb > (self.cur_val - self.nxt_mod) * self.params.nullstep_factor.max(0.5) {
warn!("Relative precision of returned objective interval enforces null-step.");
self.iterinfos.push(IterationInfo::UpperBoundNullStep);
}
}
} else if self.cur_val - nxt_lb > 0.8 * (self.cur_val - self.nxt_mod) {
// TODO: double check with ConicBundle if this test makes sense.
// lower bound gives already a null step
// subgradient won't yield much improvement
warn!("Shallow cut (subgradient won't yield much improvement)");
self.iterinfos.push(IterationInfo::ShallowCut);
}
debug!("Step");
debug!(" cur_val ={}", self.cur_val);
debug!(" nxt_mod ={}", self.nxt_mod);
debug!(" nxt_ub ={}", self.nxt_val);
debug!(" descent_bnd={}", descent_bnd);
// do a descent step or null step
if nxt_ub <= descent_bnd {
self.descent_step();
Ok(Step::Descent)
} else {
self.null_step();
Ok(Step::Null)
}
}
/**
* Return the bound on the function value that enforces a
* nullstep.
*
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