Adaptive line search algorithm (step size selection) for descent methods. function [stepsize, newx, newkey, lsstats] = linesearch_adaptive(problem, x, d, f0, df0, options, storedb, key) Adaptive linesearch algorithm for descent methods, based on a simple backtracking method. Contrary to linesearch.m, this function is not invariant under rescaling of the search direction d. These two line search methods vary mainly in their strategy to pick the initial step size. Below, the step is constructed as alpha*d, and the step size is the norm of that vector, thus: stepsize = alpha*norm_d. The step is executed by retracting the vector alpha*d from the current point x, giving newx. This line-search may create and maintain a structure called lsmem inside storedb.internal. This gives the linesearch the opportunity to remember what happened in the previous calls. This is typically used to make a first guess at the step size, based on previous events. Inputs/Outputs : see help for linesearch See also: steepestdescent conjugategradients linesearch

- StoreDB
- getCost Computes the cost function at x.
- mergeOptions Merges two options structures with one having precedence over the other.

- conjugategradient Conjugate gradient minimization algorithm for Manopt.

0001 function [stepsize, newx, newkey, lsstats] = ... 0002 linesearch_adaptive(problem, x, d, f0, df0, options, storedb, key) 0003 % Adaptive line search algorithm (step size selection) for descent methods. 0004 % 0005 % function [stepsize, newx, newkey, lsstats] = 0006 % linesearch_adaptive(problem, x, d, f0, df0, options, storedb, key) 0007 % 0008 % Adaptive linesearch algorithm for descent methods, based on a simple 0009 % backtracking method. Contrary to linesearch.m, this function is not 0010 % invariant under rescaling of the search direction d. These two line 0011 % search methods vary mainly in their strategy to pick the initial step 0012 % size. 0013 % 0014 % Below, the step is constructed as alpha*d, and the step size is the norm 0015 % of that vector, thus: stepsize = alpha*norm_d. The step is executed by 0016 % retracting the vector alpha*d from the current point x, giving newx. 0017 % 0018 % This line-search may create and maintain a structure called lsmem inside 0019 % storedb.internal. This gives the linesearch the opportunity to remember 0020 % what happened in the previous calls. This is typically used to make a 0021 % first guess at the step size, based on previous events. 0022 % 0023 % Inputs/Outputs : see help for linesearch 0024 % 0025 % See also: steepestdescent conjugategradients linesearch 0026 0027 % This file is part of Manopt: www.manopt.org. 0028 % Original author: Bamdev Mishra, Dec. 30, 2012. 0029 % Contributors: Nicolas Boumal 0030 % Change log: 0031 % 0032 % Sept. 13, 2013 (NB) : 0033 % The automatic direction reversal feature was removed (it triggered 0034 % when df0 > 0). Direction reversal is a decision that needs to be 0035 % made by the solver, so it can know about it. 0036 % 0037 % Nov. 7, 2013 (NB) : 0038 % The whole function has been recorded to mimic more closely the new 0039 % version of linesearch.m. The parameters are available through the 0040 % options structure passed to the solver and have the same names and 0041 % same meaning as for the base linesearch. The information is logged 0042 % more reliably. 0043 % 0044 % April 3, 2015 (NB): 0045 % Works with the new StoreDB class system. 0046 % 0047 % April 8, 2015 (NB): 0048 % Got rid of lsmem input/output: now maintained in storedb.internal. 0049 % 0050 % Aug. 2, 2018 (NB): 0051 % Now using storedb.remove() to keep the cache lean. 0052 0053 0054 % Allow omission of the key, and even of storedb. 0055 if ~exist('key', 'var') 0056 if ~exist('storedb', 'var') 0057 storedb = StoreDB(); 0058 end 0059 key = storedb.getNewKey(); 0060 end 0061 0062 % Backtracking default parameters. These can be overwritten in the 0063 % options structure which is passed to the solver. 0064 default_options.ls_contraction_factor = .5; 0065 default_options.ls_suff_decr = .5; 0066 default_options.ls_max_steps = 10; 0067 default_options.ls_initial_stepsize = 1; 0068 0069 if ~exist('options', 'var') || isempty(options) 0070 options = struct(); 0071 end 0072 options = mergeOptions(default_options, options); 0073 0074 contraction_factor = options.ls_contraction_factor; 0075 suff_decr = options.ls_suff_decr; 0076 max_ls_steps = options.ls_max_steps; 0077 initial_stepsize = options.ls_initial_stepsize; 0078 0079 % Compute the norm of the search direction. 0080 norm_d = problem.M.norm(x, d); 0081 0082 % If this is not the first iteration, then lsmem should have been 0083 % filled with a suggestion for the initial step. 0084 if isfield(storedb.internal, 'lsmem') 0085 lsmem = storedb.internal.lsmem; 0086 if isfield(lsmem, 'init_alpha') 0087 % Pick initial step size based on where we were last time, 0088 alpha = lsmem.init_alpha; 0089 end 0090 % Otherwise, fall back to a user supplied suggestion. 0091 else 0092 alpha = initial_stepsize / norm_d; 0093 end 0094 0095 % Make the chosen step and compute the cost there. 0096 newx = problem.M.retr(x, d, alpha); 0097 newkey = storedb.getNewKey(); 0098 newf = getCost(problem, newx, storedb, newkey); 0099 cost_evaluations = 1; 0100 0101 % Backtrack while the Armijo criterion is not satisfied 0102 while newf > f0 + suff_decr*alpha*df0 0103 0104 % Reduce the step size, 0105 alpha = contraction_factor * alpha; 0106 0107 % and look closer down the line. 0108 storedb.remove(newkey); % we no longer need this cache 0109 newx = problem.M.retr(x, d, alpha); 0110 newkey = storedb.getNewKey(); 0111 newf = getCost(problem, newx, storedb, newkey); 0112 cost_evaluations = cost_evaluations + 1; 0113 0114 % Make sure we don't run out of budget. 0115 if cost_evaluations >= max_ls_steps 0116 break; 0117 end 0118 0119 end 0120 0121 % If we got here without obtaining a decrease, we reject the step. 0122 if newf > f0 0123 alpha = 0; 0124 newx = x; 0125 newkey = key; 0126 newf = f0; %#ok<NASGU> 0127 end 0128 0129 % As seen outside this function, stepsize is the size of the vector we 0130 % retract to make the step from x to newx. Since the step is alpha*d: 0131 stepsize = alpha * norm_d; 0132 0133 % Fill lsmem with a suggestion for what the next initial step size 0134 % trial should be. On average we intend to do only one extra cost 0135 % evaluation. Notice how the suggestion is not about stepsize but about 0136 % alpha. This is the reason why this line search is not invariant under 0137 % rescaling of the search direction d. 0138 switch cost_evaluations 0139 case 1 0140 % If things go very well, push your luck. 0141 init_alpha = 2 * alpha; 0142 case 2 0143 % If things go reasonably well, try to keep pace. 0144 init_alpha = alpha; 0145 otherwise 0146 % If we backtracked a lot, the new stepsize is probably quite 0147 % small: try to recover. 0148 init_alpha = 2 * alpha; 0149 end 0150 storedb.internal.lsmem.init_alpha = init_alpha; 0151 0152 % Return some statistics also, for possible analysis. 0153 lsstats.costevals = cost_evaluations; 0154 lsstats.stepsize = stepsize; 0155 lsstats.alpha = alpha; 0156 0157 end

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