# Getting error with my codes

Hello,

I am very new on R Programming Language . I try to solve golden section search method with using R language. I solved with excel the answer should be maximum deflection is 0.5152 at x =267.40 . this problem but also I want to solve with R. I am using the script that implemented this method In the codes I indicated proper function and boundary values but the result is not I exptected. I write my codes below, I cant find which part is wrong. thank you for interest.

My problem:

``````> w0 = 2.5; E = 50000; I = 30000; L=600;
> f <- function(x)  (wo/(120*E*I*L)*(-x.^5+2*L^2*x.^3-L^4*x);
> lower.bound = 0
> upper.bound = 600
> golden.section.search = function(f, lower.bound, upper.bound, tolerance)
> {
>    golden.ratio = 2/(sqrt(5) + 1)
>
>    ### Use the golden ratio to set the initial test points
>    x1 = upper.bound - golden.ratio*(upper.bound - lower.bound)
>    x2 = lower.bound + golden.ratio*(upper.bound - lower.bound)
>
>    ### Evaluate the function at the test points
>    f1 = f(x1)
>    f2 = f(x2)
>
>    iteration = 0
>
>    while (abs(upper.bound - lower.bound) > tolerance)
>    {
>       iteration = iteration + 1
>       cat('', '\n')
>       cat('Iteration #', iteration, '\n')
>       cat('f1 =', f1, '\n')
>       cat('f2 =', f2, '\n')
>
>       if (f2 > f1)
>       # then the minimum is to the left of x2
>       # let x2 be the new upper bound
>       # let x1 be the new upper test point
>       {
>          cat('f2 > f1', '\n')
>          ### Set the new upper bound
>          upper.bound = x2
>          cat('New Upper Bound =', upper.bound, '\n')
>          cat('New Lower Bound =', lower.bound, '\n')
>          ### Set the new upper test point
>          ### Use the special result of the golden ratio
>          x2 = x1
>          cat('New Upper Test Point = ', x2, '\n')
>          f2 = f1
>
>          ### Set the new lower test point
>          x1 = upper.bound - golden.ratio*(upper.bound - lower.bound)
>          cat('New Lower Test Point = ', x1, '\n')
>          f1 = f(x1)
>       }
>       else
>       {
>          cat('f2 < f1', '\n')
>          # the minimum is to the right of x1
>          # let x1 be the new lower bound
>          # let x2 be the new lower test point
>
>          ### Set the new lower bound
>          lower.bound = x1
>          cat('New Upper Bound =', upper.bound, '\n')
>          cat('New Lower Bound =', lower.bound, '\n')
>
>          ### Set the new lower test point
>          x1 = x2
>          cat('New Lower Test Point = ', x1, '\n')
>
>          f1 = f2
>
>          ### Set the new upper test point
>          x2 = lower.bound + golden.ratio*(upper.bound - lower.bound)
>          cat('New Upper Test Point = ', x2, '\n')
>          f2 = f(x2)
>       }
>    }
>
>    ### Use the mid-point of the final interval as the estimate of the optimzer
>    cat('', '\n')
>    cat('Final Lower Bound =', lower.bound, '\n')
>    cat('Final Upper Bound =', upper.bound, '\n')
>    estimated.minimizer = (lower.bound + upper.bound)/2
>    cat('Estimated Minimizer =', estimated.minimizer, '\n')
> }
``````

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It is ok now I guess

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