alpaqa 1.0.0a10
Nonconvex constrained optimization
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panoc.hpp File Reference
#include <alpaqa/export.hpp>
#include <alpaqa/inner/directions/panoc-direction-update.hpp>
#include <alpaqa/inner/inner-solve-options.hpp>
#include <alpaqa/inner/internal/lipschitz.hpp>
#include <alpaqa/inner/internal/panoc-helpers.hpp>
#include <alpaqa/inner/internal/panoc-stop-crit.hpp>
#include <alpaqa/inner/internal/solverstatus.hpp>
#include <alpaqa/problem/type-erased-problem.hpp>
#include <alpaqa/util/atomic-stop-signal.hpp>
#include <chrono>
#include <iostream>
#include <limits>
#include <stdexcept>
#include <string>
#include <type_traits>
+ Include dependency graph for panoc.hpp:
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Go to the source code of this file.

Classes

struct  PANOCParams< Conf >
 Tuning parameters for the PANOC algorithm. More...
 
struct  PANOCStats< Conf >
 
struct  PANOCProgressInfo< Conf >
 
class  PANOCSolver< DirectionT >
 PANOC solver for ALM. More...
 
struct  InnerStatsAccumulator< PANOCStats< Conf > >
 

Namespaces

namespace  alpaqa
 

Functions

template<Config Conf>
InnerStatsAccumulator< PANOCStats< Conf > > & operator+= (InnerStatsAccumulator< PANOCStats< Conf > > &acc, const PANOCStats< Conf > &s)
 

Class Documentation

◆ alpaqa::PANOCParams

struct alpaqa::PANOCParams
+ Collaboration diagram for PANOCParams< Conf >:
Class Members
LipschitzEstimateParams< config_t > Lipschitz Parameters related to the Lipschitz constant estimate and step size.
unsigned max_iter = 100 Maximum number of inner PANOC iterations.
nanoseconds max_time = std::chrono::minutes(5) Maximum duration.
real_t min_linesearch_coefficient = real_t(1. / 256) Minimum weight factor between Newton step and projected gradient step.
bool force_linesearch = false Ignore the line search condition and always accept the accelerated step.

(For testing purposes only).

real_t linesearch_strictness_factor = real_t(0.95) Parameter β used in the line search (see Algorithm 2 in [1]).

\( 0 < \beta < 1 \)

real_t L_min = real_t(1e-5) Minimum Lipschitz constant estimate.
real_t L_max = real_t(1e20) Maximum Lipschitz constant estimate.
PANOCStopCrit stop_crit = PANOCStopCrit::ApproxKKT What stopping criterion to use.
unsigned max_no_progress = 10 Maximum number of iterations without any progress before giving up.
unsigned print_interval = 0 When to print progress.

If set to zero, nothing will be printed. If set to N != 0, progress is printed every N iterations.

int print_precision = std::numeric_limits<real_t>::max_digits10 / 2 The precision of the floating point values printed by the solver.
real_t quadratic_upperbound_tolerance_factor
real_t linesearch_tolerance_factor
bool update_direction_in_candidate = false
bool recompute_last_prox_step_after_lbfgs_flush = false

◆ alpaqa::PANOCStats

struct alpaqa::PANOCStats
+ Collaboration diagram for PANOCStats< Conf >:
Class Members
SolverStatus status = SolverStatus::Busy
real_t ε = inf<config_t>
nanoseconds elapsed_time {}
nanoseconds time_progress_callback {}
unsigned iterations = 0
unsigned linesearch_failures = 0
unsigned linesearch_backtracks = 0
unsigned stepsize_backtracks = 0
unsigned lbfgs_failures = 0
unsigned lbfgs_rejected = 0
unsigned τ_1_accepted = 0
unsigned count_τ = 0
real_t sum_τ = 0
real_t final_γ = 0
real_t final_ψ = 0
real_t final_h = 0
real_t final_φγ = 0

◆ alpaqa::PANOCProgressInfo

struct alpaqa::PANOCProgressInfo
+ Collaboration diagram for PANOCProgressInfo< Conf >:
Class Members
unsigned k
SolverStatus status
crvec x
crvec p
real_t norm_sq_p
crvec
real_t φγ
real_t ψ
crvec grad_ψ
real_t ψ_hat
crvec grad_ψ_hat
crvec q
real_t L
real_t γ
real_t τ
real_t ε
crvec Σ
crvec y
unsigned outer_iter
const TypeErasedProblem< config_t > * problem
const PANOCParams< config_t > * params

◆ alpaqa::InnerStatsAccumulator< PANOCStats< Conf > >

struct alpaqa::InnerStatsAccumulator< PANOCStats< Conf > >
+ Collaboration diagram for InnerStatsAccumulator< PANOCStats< Conf > >:
Class Members
nanoseconds elapsed_time {} Total elapsed time in the inner solver.
nanoseconds time_progress_callback {} Total time spent in the user-provided progress callback.
unsigned iterations = 0 Total number of inner PANOC iterations.
unsigned linesearch_failures = 0 Total number of PANOC line search failures.
unsigned linesearch_backtracks = 0 Total number of PANOC line search backtracking steps.
unsigned stepsize_backtracks = 0 Total number of PANOC step size reductions.
unsigned lbfgs_failures = 0 Total number of times that the L-BFGS direction was not finite.
unsigned lbfgs_rejected = 0 Total number of times that the L-BFGS update was rejected (i.e.

it could have resulted in a non-positive definite Hessian estimate).

unsigned τ_1_accepted = 0 Total number of times that a line search parameter of \( \tau = 1 \) was accepted (i.e.

no backtracking necessary).

unsigned count_τ = 0 The total number of line searches performed (used for computing the average value of \( \tau \)).
real_t sum_τ = 0 The sum of the line search parameter \( \tau \) in all iterations (used for computing the average value of \( \tau \)).
real_t final_γ = 0 The final PANOC step size γ.
real_t final_ψ = 0 Final value of the smooth cost \( \psi(\hat x) \).
real_t final_h = 0 Final value of the nonsmooth cost \( h(\hat x) \).
real_t final_φγ = 0 Final value of the forward-backward envelope, \( \varphi_\gamma(x) \) (note that this is in the point \( x \), not \( \hat x \)).