Skip to content
Go to file
Cannot retrieve contributors at this time
826 lines (754 sloc) 35.2 KB
function [x, cost, info, options] = trustregions(problem, x, options)
% Riemannian trust-regions solver for optimization on manifolds.
% function [x, cost, info, options] = trustregions(problem)
% function [x, cost, info, options] = trustregions(problem, x0)
% function [x, cost, info, options] = trustregions(problem, x0, options)
% function [x, cost, info, options] = trustregions(problem, [], options)
% This is the Riemannian Trust-Region solver (with tCG inner solve), named
% RTR. This solver will attempt to minimize the cost function described in
% the problem structure. It requires the availability of the cost function
% and of its gradient. It will issue calls for the Hessian. If no Hessian
% nor approximate Hessian is provided, a standard approximation of the
% Hessian based on the gradient will be computed. If a preconditioner for
% the Hessian is provided, it will be used.
% If no gradient is provided, an approximation of the gradient is computed,
% but this can be slow for manifolds of high dimension.
% For a description of the algorithm and theorems offering convergence
% guarantees, see the references below. Documentation for this solver is
% available online at:
% The initial iterate is x0 if it is provided. Otherwise, a random point on
% the manifold is picked. To specify options whilst not specifying an
% initial iterate, give x0 as [] (the empty matrix).
% The two outputs 'x' and 'cost' are the last reached point on the manifold
% and its cost. Notice that x is not necessarily the best reached point,
% because this solver is not forced to be a descent method. In particular,
% very close to convergence, it is sometimes preferable to accept very
% slight increases in the cost value (on the order of the machine epsilon)
% in the process of reaching fine convergence.
% The output 'info' is a struct-array which contains information about the
% iterations:
% iter (integer)
% The (outer) iteration number, or number of steps considered
% (whether accepted or rejected). The initial guess is 0.
% cost (double)
% The corresponding cost value.
% gradnorm (double)
% The (Riemannian) norm of the gradient.
% numinner (integer)
% The number of inner iterations executed to compute this iterate.
% Inner iterations are truncated-CG steps. Each one requires a
% Hessian (or approximate Hessian) evaluation.
% time (double)
% The total elapsed time in seconds to reach the corresponding cost.
% rho (double)
% The performance ratio for the iterate.
% rhonum, rhoden (double)
% Regularized numerator and denominator of the performance ratio:
% rho = rhonum/rhoden. See options.rho_regularization.
% accepted (boolean)
% Whether the proposed iterate was accepted or not.
% stepsize (double)
% The (Riemannian) norm of the vector returned by the inner solver
% tCG and which is retracted to obtain the proposed next iterate. If
% accepted = true for the corresponding iterate, this is the size of
% the step from the previous to the new iterate. If accepted is
% false, the step was not executed and this is the size of the
% rejected step.
% Delta (double)
% The trust-region radius at the outer iteration.
% cauchy (boolean)
% Whether the Cauchy point was used or not (if useRand is true).
% And possibly additional information logged by options.statsfun.
% For example, type [info.gradnorm] to obtain a vector of the successive
% gradient norms reached at each (outer) iteration.
% The options structure is used to overwrite the default values. All
% options have a default value and are hence optional. To force an option
% value, pass an options structure with a field options.optionname, where
% optionname is one of the following and the default value is indicated
% between parentheses:
% tolgradnorm (1e-6)
% The algorithm terminates if the norm of the gradient drops below
% this. For well-scaled problems, a rule of thumb is that you can
% expect to reduce the gradient norm by 8 orders of magnitude
% (sqrt(eps)) compared to the gradient norm at a "typical" point (a
% rough initial iterate for example). Further decrease is sometimes
% possible, but inexact floating point arithmetic will eventually
% limit the final accuracy. If tolgradnorm is set too low, the
% algorithm may end up iterating forever (or at least until another
% stopping criterion triggers).
% maxiter (1000)
% The algorithm terminates if maxiter (outer) iterations were executed.
% maxtime (Inf)
% The algorithm terminates if maxtime seconds elapsed.
% miniter (3)
% Minimum number of outer iterations (used only if useRand is true).
% mininner (1)
% Minimum number of inner iterations (for tCG).
% maxinner (problem.M.dim() : the manifold's dimension)
% Maximum number of inner iterations (for tCG).
% Delta_bar (problem.M.typicaldist() or sqrt(problem.M.dim()))
% Maximum trust-region radius. If you specify this parameter but not
% Delta0, then Delta0 will be set to 1/8 times this parameter.
% Delta0 (Delta_bar/8)
% Initial trust-region radius. If you observe a long plateau at the
% beginning of the convergence plot (gradient norm VS iteration), it
% may pay off to try to tune this parameter to shorten the plateau.
% You should not set this parameter without setting Delta_bar too (at
% a larger value).
% useRand (false)
% Set to true if the trust-region solve is to be initiated with a
% random tangent vector. If set to true, no preconditioner will be
% used. This option is set to true in some scenarios to escape saddle
% points, but is otherwise seldom activated.
% kappa (0.1)
% tCG inner kappa convergence tolerance.
% kappa > 0 is the linear convergence target rate: tCG will terminate
% early if the residual was reduced by a factor of kappa.
% theta (1.0)
% tCG inner theta convergence tolerance.
% 1+theta (theta between 0 and 1) is the superlinear convergence
% target rate. tCG will terminate early if the residual was reduced
% by a power of 1+theta.
% rho_prime (0.1)
% Accept/reject threshold : if rho is at least rho_prime, the outer
% iteration is accepted. Otherwise, it is rejected. In case it is
% rejected, the trust-region radius will have been decreased.
% To ensure this, rho_prime >= 0 must be strictly smaller than 1/4.
% If rho_prime is negative, the algorithm is not guaranteed to
% produce monotonically decreasing cost values. It is strongly
% recommended to set rho_prime > 0, to aid convergence.
% rho_regularization (1e3)
% Close to convergence, evaluating the performance ratio rho is
% numerically challenging. Meanwhile, close to convergence, the
% quadratic model should be a good fit and the steps should be
% accepted. Regularization lets rho go to 1 as the model decrease and
% the actual decrease go to zero. Set this option to zero to disable
% regularization (not recommended). See in-code for the specifics.
% When this is not zero, it may happen that the iterates produced are
% not monotonically improving the cost when very close to
% convergence. This is because the corrected cost improvement could
% change sign if it is negative but very small.
% statsfun (none)
% Function handle to a function that will be called after each
% iteration to provide the opportunity to log additional statistics.
% They will be returned in the info struct. See the generic Manopt
% documentation about solvers for further information. statsfun is
% called with the point x that was reached last, after the
% accept/reject decision. See comment below.
% stopfun (none)
% Function handle to a function that will be called at each iteration
% to provide the opportunity to specify additional stopping criteria.
% See the generic Manopt documentation about solvers for further
% information.
% verbosity (2)
% Integer number used to tune the amount of output the algorithm
% generates during execution (mostly as text in the command window).
% The higher, the more output. 0 means silent. 3 and above includes a
% display of the options structure at the beginning of the execution.
% debug (false)
% Set to true to allow the algorithm to perform additional
% computations for debugging purposes. If a debugging test fails, you
% will be informed of it, usually via the command window. Be aware
% that these additional computations appear in the algorithm timings
% too, and may interfere with operations such as counting the number
% of cost evaluations, etc. (the debug calls get storedb too).
% storedepth (2)
% Maximum number of different points x of the manifold for which a
% store structure will be kept in memory in the storedb for caching.
% If memory usage is an issue, you may try to lower this number.
% Profiling or manopt counters may then help to investigate if a
% performance hit was incurred as a result.
% hook (none)
% A function handle which allows the user to change the current point
% x at the beginning of each iteration, before the stopping criterion
% is evaluated. See applyHook for help on how to use this option.
% Notice that statsfun is called with the point x that was reached last,
% after the accept/reject decision. Hence: if the step was accepted, we get
% that new x, with a store which only saw the call for the cost and for the
% gradient. If the step was rejected, we get the same x as previously, with
% the store structure containing everything that was computed at that point
% (possibly including previous rejects at that same point). Hence, statsfun
% should not be used in conjunction with the store to count operations for
% example. Instead, you should use manopt counters: see statscounters.
% Please cite the Manopt paper as well as the research paper:
% @Article{genrtr,
% Title = {Trust-region methods on {Riemannian} manifolds},
% Author = {Absil, P.-A. and Baker, C. G. and Gallivan, K. A.},
% Journal = {Foundations of Computational Mathematics},
% Year = {2007},
% Number = {3},
% Pages = {303--330},
% Volume = {7},
% Doi = {10.1007/s10208-005-0179-9}
% }
% See also: steepestdescent conjugategradient manopt/examples
% An explicit, general listing of this algorithm, with preconditioning,
% can be found in the following paper:
% @Article{boumal2015lowrank,
% Title = {Low-rank matrix completion via preconditioned optimization on the {G}rassmann manifold},
% Author = {Boumal, N. and Absil, P.-A.},
% Journal = {Linear Algebra and its Applications},
% Year = {2015},
% Pages = {200--239},
% Volume = {475},
% Doi = {10.1016/j.laa.2015.02.027},
% }
% When the Hessian is not specified, it is approximated with
% finite-differences of the gradient. The resulting method is called
% RTR-FD. Some convergence theory for it is available in this paper:
% @incollection{boumal2015rtrfd
% author={Boumal, N.},
% title={Riemannian trust regions with finite-difference Hessian approximations are globally convergent},
% year={2015},
% booktitle={Geometric Science of Information}
% }
% This file is part of Manopt:
% This code is an adaptation to Manopt of the original GenRTR code:
% RTR - Riemannian Trust-Region
% (c) 2004-2007, P.-A. Absil, C. G. Baker, K. A. Gallivan
% Florida State University
% School of Computational Science
% (
% See accompanying license file.
% The adaptation was executed by Nicolas Boumal.
% Change log:
% NB April 3, 2013:
% tCG now returns the Hessian along the returned direction eta, so
% that we do not compute that Hessian redundantly: some savings at
% each iteration. Similarly, if the useRand flag is on, we spare an
% extra Hessian computation at each outer iteration too, owing to
% some modifications in the Cauchy point section of the code specific
% to useRand = true.
% NB Aug. 22, 2013:
% This function is now Octave compatible. The transition called for
% two changes which would otherwise not be advisable. (1) tic/toc is
% now used as is, as opposed to the safer way:
% t = tic(); elapsed = toc(t);
% And (2), the (formerly inner) function savestats was moved outside
% the main function to not be nested anymore. This is arguably less
% elegant, but Octave does not (and likely will not) support nested
% functions.
% NB Dec. 2, 2013:
% The in-code documentation was largely revised and expanded.
% NB Dec. 2, 2013:
% The former heuristic which triggered when rhonum was very small and
% forced rho = 1 has been replaced by a smoother heuristic which
% consists in regularizing rhonum and rhoden before computing their
% ratio. It is tunable via options.rho_regularization. Furthermore,
% the solver now detects if tCG did not obtain a model decrease
% (which is theoretically impossible but may happen because of
% numerical errors and/or because of a nonlinear/nonsymmetric Hessian
% operator, which is the case for finite difference approximations).
% When such an anomaly is detected, the step is rejected and the
% trust region radius is decreased.
% Feb. 18, 2015 note: this is less useful now, as tCG now guarantees
% model decrease even for the finite difference approximation of the
% Hessian. It is still useful in case of numerical errors, but this
% is less stringent.
% NB Dec. 3, 2013:
% The stepsize is now registered at each iteration, at a small
% additional cost. The defaults for Delta_bar and Delta0 are better
% defined. Setting Delta_bar in the options will automatically set
% Delta0 accordingly. In Manopt 1.0.4, the defaults for these options
% were not treated appropriately because of an incorrect use of the
% isfield() built-in function.
% NB Feb. 18, 2015:
% Added some comments. Also, Octave now supports safe tic/toc usage,
% so we reverted the changes to use that again (see Aug. 22, 2013 log
% entry).
% NB April 3, 2015:
% Works with the new StoreDB class system.
% NB April 8, 2015:
% No Hessian warning if approximate Hessian explicitly available.
% NB Nov. 1, 2016:
% Now uses approximate gradient via finite differences if need be.
% NB Aug. 2, 2018:
% Using storedb.remove() to keep the cache lean, which allowed to
% reduce storedepth to 2 from 20 (by default).
% NB July 19, 2020:
% Added support for options.hook.
M = problem.M;
% Verify that the problem description is sufficient for the solver.
if ~canGetCost(problem)
warning('manopt:getCost', ...
'No cost provided. The algorithm will likely abort.');
if ~canGetGradient(problem) && ~canGetApproxGradient(problem)
% Note: we do not give a warning if an approximate gradient is
% explicitly given in the problem description, as in that case the user
% seems to be aware of the issue.
warning('manopt:getGradient:approx', ...
['No gradient provided. Using an FD approximation instead (slow).\n' ...
'It may be necessary to increase options.tolgradnorm.\n' ...
'To disable this warning: warning(''off'', ''manopt:getGradient:approx'')']);
problem.approxgrad = approxgradientFD(problem);
if ~canGetHessian(problem) && ~canGetApproxHessian(problem)
% Note: we do not give a warning if an approximate Hessian is
% explicitly given in the problem description, as in that case the user
% seems to be aware of the issue.
warning('manopt:getHessian:approx', ...
['No Hessian provided. Using an FD approximation instead.\n' ...
'To disable this warning: warning(''off'', ''manopt:getHessian:approx'')']);
problem.approxhess = approxhessianFD(problem);
% Define some strings for display
tcg_stop_reason = {'negative curvature',...
'exceeded trust region',...
'reached target residual-kappa (linear)',...
'reached target residual-theta (superlinear)',...
'maximum inner iterations',...
'model increased'};
% Set local defaults here
localdefaults.verbosity = 2;
localdefaults.maxtime = inf;
localdefaults.miniter = 3;
localdefaults.maxiter = 1000;
localdefaults.mininner = 1;
localdefaults.maxinner = M.dim();
localdefaults.tolgradnorm = 1e-6;
localdefaults.kappa = 0.1;
localdefaults.theta = 1.0;
localdefaults.rho_prime = 0.1;
localdefaults.useRand = false;
localdefaults.rho_regularization = 1e3;
% Merge global and local defaults, then merge w/ user options, if any.
localdefaults = mergeOptions(getGlobalDefaults(), localdefaults);
if ~exist('options', 'var') || isempty(options)
options = struct();
options = mergeOptions(localdefaults, options);
% Set default Delta_bar and Delta0 separately to deal with additional
% logic: if Delta_bar is provided but not Delta0, let Delta0 automatically
% be some fraction of the provided Delta_bar.
if ~isfield(options, 'Delta_bar')
if isfield(M, 'typicaldist')
options.Delta_bar = M.typicaldist();
options.Delta_bar = sqrt(M.dim());
if ~isfield(options,'Delta0')
options.Delta0 = options.Delta_bar / 8;
% Check some option values
assert(options.rho_prime < 1/4, ...
'options.rho_prime must be strictly smaller than 1/4.');
assert(options.Delta_bar > 0, ...
'options.Delta_bar must be positive.');
assert(options.Delta0 > 0 && options.Delta0 <= options.Delta_bar, ...
'options.Delta0 must be positive and smaller than Delta_bar.');
% It is sometimes useful to check what the actual option values are.
if options.verbosity >= 3
ticstart = tic();
% If no initial point x is given by the user, generate one at random.
if ~exist('x', 'var') || isempty(x)
x = M.rand();
% Create a store database and get a key for the current x
storedb = StoreDB(options.storedepth);
key = storedb.getNewKey();
%% Initializations
% k counts the outer (TR) iterations. The semantic is that k counts the
% number of iterations fully executed so far.
k = 0;
% Initialize solution and companion measures: f(x), fgrad(x)
[fx, fgradx] = getCostGrad(problem, x, storedb, key);
norm_grad = M.norm(x, fgradx);
% Initialize trust-region radius
Delta = options.Delta0;
% Save stats in a struct array info, and preallocate.
if ~exist('used_cauchy', 'var')
used_cauchy = [];
stats = savestats(problem, x, storedb, key, options, k, fx, norm_grad, Delta, ticstart);
info(1) = stats;
info(min(10000, options.maxiter+1)).iter = [];
% ** Display:
if options.verbosity == 2
fprintf(['%3s %3s %5s %5s ',...
'f: %+e |grad|: %e\n'],...
' ',' ',' ',' ', fx, norm_grad);
elseif options.verbosity > 2
fprintf('%3s %3s k: %5s num_inner: %5s %s\n',...
fprintf(' f(x) : %+e |grad| : %e\n', fx, norm_grad);
fprintf(' Delta : %f\n', Delta);
% To keep track of consecutive radius changes, so that we can warn the
% user if it appears necessary.
consecutive_TRplus = 0;
consecutive_TRminus = 0;
% **********************
% ** Start of TR loop **
% **********************
while true
% Start clock for this outer iteration
ticstart = tic();
% Apply the hook function if there is one: this allows external code to
% move x to another point. If the point is changed (indicated by a true
% value for the boolean 'hooked'), we update our knowledge about x.
[x, key, info, hooked] = applyHook(problem, x, storedb, key, options, info, k+1);
if hooked
[fx, fgradx] = getCostGrad(problem, x, storedb, key);
norm_grad = M.norm(x, fgradx);
% Run standard stopping criterion checks
[stop, reason] = stoppingcriterion(problem, x, options, info, k+1);
% If the stopping criterion that triggered is the tolerance on the
% gradient norm but we are using randomization, make sure we make at
% least miniter iterations to give randomization a chance at escaping
% saddle points.
if stop == 2 && options.useRand && k < options.miniter
stop = 0;
if stop
if options.verbosity >= 1
fprintf([reason '\n']);
if options.verbosity > 2 || options.debug > 0
% *************************
% ** Begin TR Subproblem **
% *************************
% Determine eta0
if ~options.useRand
% Pick the zero vector
eta = M.zerovec(x);
% Random vector in T_x M (this has to be very small)
eta = M.lincomb(x, 1e-6, M.randvec(x));
% Must be inside trust-region
while M.norm(x, eta) > Delta
eta = M.lincomb(x, sqrt(sqrt(eps)), eta);
% Solve TR subproblem approximately
[eta, Heta, numit, stop_inner] = ...
tCG(problem, x, fgradx, eta, Delta, options, storedb, key);
srstr = tcg_stop_reason{stop_inner};
% If using randomized approach, compare result with the Cauchy point.
% Convergence proofs assume that we achieve at least (a fraction of)
% the reduction of the Cauchy point. After this if-block, either all
% eta-related quantities have been changed consistently, or none of
% them have changed.
if options.useRand
used_cauchy = false;
% Check the curvature,
Hg = getHessian(problem, x, fgradx, storedb, key);
g_Hg = M.inner(x, fgradx, Hg);
if g_Hg <= 0
tau_c = 1;
tau_c = min( norm_grad^3/(Delta*g_Hg) , 1);
% and generate the Cauchy point.
eta_c = M.lincomb(x, -tau_c * Delta / norm_grad, fgradx);
Heta_c = M.lincomb(x, -tau_c * Delta / norm_grad, Hg);
% Now that we have computed the Cauchy point in addition to the
% returned eta, we might as well keep the best of them.
mdle = fx + M.inner(x, fgradx, eta) + .5*M.inner(x, Heta, eta);
mdlec = fx + M.inner(x, fgradx, eta_c) + .5*M.inner(x, Heta_c, eta_c);
if mdlec < mdle
eta = eta_c;
Heta = Heta_c; % added April 11, 2012
used_cauchy = true;
% This is computed for logging purposes and may be useful for some
% user-defined stopping criteria.
norm_eta = M.norm(x, eta);
if options.debug > 0
testangle = M.inner(x, eta, fgradx) / (norm_eta*norm_grad);
% Compute the tentative next iterate (the proposal)
x_prop = M.retr(x, eta);
key_prop = storedb.getNewKey();
% Compute the function value of the proposal
fx_prop = getCost(problem, x_prop, storedb, key_prop);
% Will we accept the proposal or not?
% Check the performance of the quadratic model against the actual cost.
rhonum = fx - fx_prop;
vecrho = M.lincomb(x, 1, fgradx, .5, Heta);
rhoden = -M.inner(x, eta, vecrho);
% rhonum could be anything.
% rhoden should be nonnegative, as guaranteed by tCG, baring numerical
% errors.
% Heuristic -- added Dec. 2, 2013 (NB) to replace the former heuristic.
% This heuristic is documented in the book by Conn Gould and Toint on
% trust-region methods, section 17.4.2.
% rhonum measures the difference between two numbers. Close to
% convergence, these two numbers are very close to each other, so
% that computing their difference is numerically challenging: there may
% be a significant loss in accuracy. Since the acceptance or rejection
% of the step is conditioned on the ratio between rhonum and rhoden,
% large errors in rhonum result in a very large error in rho, hence in
% erratic acceptance / rejection. Meanwhile, close to convergence,
% steps are usually trustworthy and we should transition to a Newton-
% like method, with rho=1 consistently. The heuristic thus shifts both
% rhonum and rhoden by a small amount such that far from convergence,
% the shift is irrelevant and close to convergence, the ratio rho goes
% to 1, effectively promoting acceptance of the step.
% The rationale is that close to convergence, both rhonum and rhoden
% are quadratic in the distance between x and x_prop. Thus, when this
% distance is on the order of sqrt(eps), the value of rhonum and rhoden
% is on the order of eps, which is indistinguishable from the numerical
% error, resulting in badly estimated rho's.
% For abs(fx) < 1, this heuristic is invariant under offsets of f but
% not under scaling of f. For abs(fx) > 1, the opposite holds. This
% should not alarm us, as this heuristic only triggers at the very last
% iterations if very fine convergence is demanded.
rho_reg = max(1, abs(fx)) * eps * options.rho_regularization;
rhonum = rhonum + rho_reg;
rhoden = rhoden + rho_reg;
if options.debug > 0
fprintf('DBG: rhonum : %e\n', rhonum);
fprintf('DBG: rhoden : %e\n', rhoden);
% This is always true if a linear, symmetric operator is used for the
% Hessian (approximation) and if we had infinite numerical precision.
% In practice, nonlinear approximations of the Hessian such as the
% built-in finite difference approximation and finite numerical
% accuracy can cause the model to increase. In such scenarios, we
% decide to force a rejection of the step and a reduction of the
% trust-region radius. We test the sign of the regularized rhoden since
% the regularization is supposed to capture the accuracy to which
% rhoden is computed: if rhoden were negative before regularization but
% not after, that should not be (and is not) detected as a failure.
% Note (Feb. 17, 2015, NB): the most recent version of tCG already
% includes a mechanism to ensure model decrease if the Cauchy step
% attained a decrease (which is theoretically the case under very lax
% assumptions). This being said, it is always possible that numerical
% errors will prevent this, so that it is good to keep a safeguard.
% The current strategy is that, if this should happen, then we reject
% the step and reduce the trust region radius. This also ensures that
% the actual cost values are monotonically decreasing.
% [This bit of code seems not to trigger because tCG already ensures
% the model decreases even in the presence of non-linearities; but as
% a result the radius is not necessarily decreased. Perhaps we should
% change this with the proposed commented line below; needs testing.]
model_decreased = (rhoden >= 0);
% model_decreased = (rhoden >= 0) && (stop_inner ~= 6);
if ~model_decreased
srstr = [srstr ', model did not decrease']; %#ok<AGROW>
rho = rhonum / rhoden;
% Added June 30, 2015 following observation by BM.
% With this modification, it is guaranteed that a step rejection is
% always accompanied by a TR reduction. This prevents stagnation in
% this "corner case" (NaN's really aren't supposed to occur, but it's
% nice if we can handle them nonetheless).
if isnan(rho)
fprintf('rho is NaN! Forcing a radius decrease. This should not happen.\n');
if isnan(fx_prop)
fprintf('The cost function returned NaN (perhaps the retraction returned a bad point?)\n');
fprintf('The cost function did not return a NaN value.\n');
if options.debug > 0
m = @(x, eta) ...
getCost(problem, x, storedb, key) + ...
getDirectionalDerivative(problem, x, eta, storedb, key) + ...
.5*M.inner(x, getHessian(problem, x, eta, storedb, key), eta);
zerovec = M.zerovec(x);
actrho = (fx - fx_prop) / (m(x, zerovec) - m(x, eta));
fprintf('DBG: new f(x) : %+e\n', fx_prop);
fprintf('DBG: actual rho : %e\n', actrho);
fprintf('DBG: used rho : %e\n', rho);
% Choose the new TR radius based on the model performance
trstr = ' ';
% If the actual decrease is smaller than 1/4 of the predicted decrease,
% then reduce the TR radius.
if rho < 1/4 || ~model_decreased || isnan(rho)
trstr = 'TR-';
Delta = Delta/4;
consecutive_TRplus = 0;
consecutive_TRminus = consecutive_TRminus + 1;
if consecutive_TRminus >= 5 && options.verbosity >= 2
consecutive_TRminus = -inf;
fprintf(' +++ Detected many consecutive TR- (radius decreases).\n');
fprintf(' +++ Consider decreasing options.Delta_bar by an order of magnitude.\n');
fprintf(' +++ Current values: options.Delta_bar = %g and options.Delta0 = %g.\n', options.Delta_bar, options.Delta0);
% If the actual decrease is at least 3/4 of the predicted decrease and
% the tCG (inner solve) hit the TR boundary, increase the TR radius.
% We also keep track of the number of consecutive trust-region radius
% increases. If there are many, this may indicate the need to adapt the
% initial and maximum radii.
elseif rho > 3/4 && (stop_inner == 1 || stop_inner == 2)
trstr = 'TR+';
Delta = min(2*Delta, options.Delta_bar);
consecutive_TRminus = 0;
consecutive_TRplus = consecutive_TRplus + 1;
if consecutive_TRplus >= 5 && options.verbosity >= 1
consecutive_TRplus = -inf;
fprintf(' +++ Detected many consecutive TR+ (radius increases).\n');
fprintf(' +++ Consider increasing options.Delta_bar by an order of magnitude.\n');
fprintf(' +++ Current values: options.Delta_bar = %g and options.Delta0 = %g.\n', options.Delta_bar, options.Delta0);
% Otherwise, keep the TR radius constant.
consecutive_TRplus = 0;
consecutive_TRminus = 0;
% Choose to accept or reject the proposed step based on the model
% performance. Note the strict inequality.
if model_decreased && rho > options.rho_prime
% April 17, 2018: a side effect of rho_regularization > 0 is that
% it can happen that the cost function appears to go up (although
% only by a small amount) for some accepted steps. We decide to
% accept this because, numerically, computing the difference
% between fx_prop and fx is more difficult than computing the
% improvement in the model, because fx_prop and fx are on the same
% order of magnitude yet are separated by a very small gap near
% convergence, whereas the model improvement is computed as a sum
% of two small terms. As a result, the step which seems bad may
% turn out to be good, in that it may help reduce the gradient norm
% for example. This update merely informs the user of this event.
% In further updates, we could also introduce this as a stopping
% criterion. It is then important to choose wisely which of x or
% x_prop should be returned (perhaps the one with smallest
% gradient?)
if fx_prop > fx && options.verbosity >= 2
fprintf(['Between line above and below, cost function ' ...
'increased by %s (step size: %g)\n'], ...
fx_prop - fx, norm_eta);
accept = true;
accstr = 'acc';
% We accept the step: no need to keep the old cache.
storedb.removefirstifdifferent(key, key_prop);
x = x_prop;
key = key_prop;
fx = fx_prop;
fgradx = getGradient(problem, x, storedb, key);
norm_grad = M.norm(x, fgradx);
% We reject the step: no need to keep cache related to the
% tentative step.
storedb.removefirstifdifferent(key_prop, key);
accept = false;
accstr = 'REJ';
% k is the number of iterations we have accomplished.
k = k + 1;
% Make sure we don't use too much memory for the store database
% Log statistics for freshly executed iteration.
% Everything after this in the loop is not accounted for in the timing.
stats = savestats(problem, x, storedb, key, options, k, fx, ...
norm_grad, Delta, ticstart, info, rho, rhonum, ...
rhoden, accept, numit, norm_eta, used_cauchy);
info(k+1) = stats;
% ** Display:
if options.verbosity == 2
fprintf(['%3s %3s k: %5d num_inner: %5d ', ...
'f: %+e |grad|: %e %s\n'], ...
elseif options.verbosity > 2
if options.useRand && used_cauchy
fprintf('USED CAUCHY POINT\n');
fprintf('%3s %3s k: %5d num_inner: %5d %s\n', ...
accstr, trstr, k, numit, srstr);
fprintf(' f(x) : %+e |grad| : %e\n',fx,norm_grad);
if options.debug > 0
fprintf(' Delta : %f |eta| : %e\n',Delta,norm_eta);
fprintf(' rho : %e\n',rho);
if options.debug > 0
fprintf('DBG: cos ang(eta,gradf): %d\n',testangle);
if rho == 0
fprintf('DBG: rho = 0, this will likely hinder further convergence.\n');
end % of TR loop (counter: k)
% Restrict info struct-array to useful part
info = info(1:k+1);
if (options.verbosity > 2) || (options.debug > 0)
if (options.verbosity > 0) || (options.debug > 0)
fprintf('Total time is %f [s] (excludes statsfun)\n', info(end).time);
% Return the best cost reached
cost = fx;
% Routine in charge of collecting the current iteration stats
function stats = savestats(problem, x, storedb, key, options, k, fx, ...
norm_grad, Delta, ticstart, info, rho, rhonum, ...
rhoden, accept, numit, norm_eta, used_cauchy)
stats.iter = k;
stats.cost = fx;
stats.gradnorm = norm_grad;
stats.Delta = Delta;
if k == 0
stats.time = toc(ticstart);
stats.rho = inf;
stats.rhonum = NaN;
stats.rhoden = NaN;
stats.accepted = true;
stats.numinner = 0;
stats.stepsize = NaN;
if options.useRand
stats.cauchy = false;
stats.time = info(k).time + toc(ticstart);
stats.rho = rho;
stats.rhonum = rhonum;
stats.rhoden = rhoden;
stats.accepted = accept;
stats.numinner = numit;
stats.stepsize = norm_eta;
if options.useRand
stats.cauchy = used_cauchy;
% See comment about statsfun above: the x and store passed to statsfun
% are that of the most recently accepted point after the iteration
% fully executed.
stats = applyStatsfun(problem, x, storedb, key, options, stats);
You can’t perform that action at this time.