大家好，我正在尝试为一个函数实现梯度下降算法：
我的算法起始点是 w = (u,v) = (2,2)。学习率 eta = 0.01，界限 bound = 10^-14。以下是我的MATLAB代码：
function [resultTable, boundIter] = gradientDescent(w, iters, bound, eta)% FUNCTION [resultTable, boundIter] = gradientDescent(w, its, bound, eta)% % DESCRIPTION: % - This function will do gradient descent error minimization for the% function E(u,v) = (u*exp(v) - 2*v*exp(-u))^2.%% INPUTS: % 'w' a 1-by-2 vector indicating initial weights w = [u,v]% 'its' a positive integer indicating the number of gradient descent% iterations% 'bound' a real number indicating an error lower bound% 'eta' a positive real number indicating the learning rate of GD algorithm%% OUTPUTS: % 'resultTable' a iters+1-by-6 table indicating the error, partial% derivatives and weights for each GD iteration% 'boundIter' a positive integer specifying the GD iteration when the error% function got below the given error bound 'bound'% % The error function E = @(u,v) (u*exp(v) - 2*v*exp(-u))^2;% Partial derivative of E with respect to u pEpu = @(u,v) 2*(u*exp(v) - 2*v*exp(-u))*(exp(v) + 2*v*exp(-u));% Partial derivative of E with respect to v pEpv = @(u,v) 2*(u*exp(v) - 2*v*exp(-u))*(u*exp(v) - 2*exp(-u));% Initialize boundIterboundIter = 0;% Create a table for holding the resultsresultTable = zeros(iters+1, 6);% Iteration numberresultTable(1, 1) = 0;% Error at iteration iresultTable(1, 2) = E(w(1), w(2));% The value of pEpu at initial w = (u,v)resultTable(1, 3) = pEpu(w(1), w(2));% The value of pEpv at initial w = (u,v)resultTable(1, 4) = pEpv(w(1), w(2));% Initial uresultTable(1, 5) = w(1);% Initial vresultTable(1, 6) = w(2);% Loop all the iterationsfor i = 2:iters+1 % Save the iteration number resultTable(i, 1) = i-1; % Update the weights temp1 = w(1) - eta*(pEpu(w(1), w(2))); temp2 = w(2) - eta*(pEpv(w(1), w(2))); w(1) = temp1; w(2) = temp2; % Evaluate the error function at new weights resultTable(i, 2) = E(w(1), w(2)); % Evaluate pEpu at the new point resultTable(i, 3) = pEpu(w(1), w(2)); % Evaluate pEpv at the new point resultTable(i, 4) = pEpv(w(1), w(2)); % Save the new weights resultTable(i, 5) = w(1); resultTable(i, 6) = w(2); % If the error function is below a specified bound save this iteration % index if E(w(1), w(2)) < bound boundIter = i-1; endend这是我机器学习课程中的一个练习，但不知为何我的结果全部错误。代码中肯定有问题。我已经尝试了多次调试但没有发现任何问题…有人能帮我找出问题所在吗？…换句话说，你能检查一下这段代码是否是给定函数的有效梯度下降算法吗？
如果我的问题不够清楚或你需要更多信息，请告诉我:)
感谢你的努力和帮助！=)
这是我五次迭代的结果以及其他人得到的结果：
参数：w = [2,2]，eta = 0.01，bound = 10^-14，iters = 5
回答：
如问题下方所讨论：我认为其他人是错的…你的最小化导致了更小的E(u,v)值，检查一下：
E(1.4,1.6) = 37.8 >> 3.6 = E(0.63, -1.67)