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thomson_problem

PURPOSE ^

Simple attempt at computing n well distributed points on a sphere in R^d.

SYNOPSIS ^

function X = thomson_problem(n, d)

DESCRIPTION ^

 Simple attempt at computing n well distributed points on a sphere in R^d.
 
 This is an example of how Manopt can approximate the gradient and even
 the Hessian of a cost function based on finite differences, even if only
 the cost function is specified without its derivatives.

 This functionality is provided only as a help for prototyping, and should
 not be used to compare algorithms in terms of computation time or
 accuracy, because the underlying gradient approximation scheme is slow.

 See also the derivative free solvers for an alternative:
 pso and neldermead.

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 function X = thomson_problem(n, d)
0002 % Simple attempt at computing n well distributed points on a sphere in R^d.
0003 %
0004 % This is an example of how Manopt can approximate the gradient and even
0005 % the Hessian of a cost function based on finite differences, even if only
0006 % the cost function is specified without its derivatives.
0007 %
0008 % This functionality is provided only as a help for prototyping, and should
0009 % not be used to compare algorithms in terms of computation time or
0010 % accuracy, because the underlying gradient approximation scheme is slow.
0011 %
0012 % See also the derivative free solvers for an alternative:
0013 % pso and neldermead.
0014 
0015 % This file is part of Manopt: www.manopt.org.
0016 % Original author: Nicolas Boumal, Nov. 1, 2016
0017 % Contributors:
0018 % Change log:
0019 
0020 if ~exist('n', 'var') || isempty(n)
0021     n = 50;
0022 end
0023 if ~exist('d', 'var') || isempty(d)
0024     d = 3;
0025 end
0026 
0027 % Define the Thomson problem with 1/r^2 potential. That is: find n points
0028 % x_i on a sphere in R^d such that the sum over all pairs (i, j) of the
0029 % potentials 1/||x_i - x_j||^2 is minimized. Since the points are on a
0030 % sphere, each potential is .5/(1-x_i'*x_j).
0031 problem.M = obliquefactory(d, n);
0032 problem.cost = @(X) sum(sum(triu(1./(1-X'*X), 1))) / n^2;
0033 
0034 % Attempt to minimize the cost. Since the gradient is not provided, Manopt
0035 % approximates it with finite differences. This is /slow/, since for each
0036 % gradient approximation, problem.M.dim()+1 calls to the cost function are
0037 % necessary, on top of generating an orthonormal basis of the tangent space
0038 % at each iterate.
0039 %
0040 % Note that it is difficult to reach high accuracy critical points with an
0041 % approximate gradient, hence it may be required to set a less ambitious
0042 % value for the gradient norm tolerance.
0043 opts.tolgradnorm = 1e-4;
0044 
0045 % Pick a solver. Both work fairly well on this problem.
0046 % X = conjugategradient(problem, [], opts);
0047 X = rlbfgs(problem, [], opts);
0048 
0049 % Plot the points on a translucid sphere
0050 if nargout == 0 && d == 3
0051     [x, y, z] = sphere(50);
0052     surf(x, y, z, 'FaceAlpha', .5);
0053     hold all;
0054     plot3(X(1, :), X(2, :), X(3, :), '.', 'MarkerSize', 20);
0055     axis equal;
0056     box off;
0057     axis off;
0058 end
0059 
0060 % For much better performance, after an early prototyping phase, the
0061 % gradient of the cost function should be specified, typically in
0062 % problem.grad or problem.egrad. See the online document at
0063 %
0064 %   http://www.manopt.org
0065 %
0066 % for more information.
0067 
0068 end

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