Minimize convex function without gradient

minimize_convex_function_no_grad.py

n_iter = 0
res_list = []


def func(x):
    global n_iter, res_list
    n_iter += 1
    res = (x + 1)**2
    res_list.append((x, res))
    return res


g = -3
d = 0
fg = func(g)
fd = func(d)
c = (g + d) / 2
fc = func(c)

max_iter = 10

while n_iter <= max_iter:
    if n_iter % 2 == 0:
        gc = (g + c) / 2
        fgc = func(gc)
        if fgc > fc:
            g = gc
            fg = fgc
        else:
            d = c
            fd = fc
            c = gc
            fc = fgc
    else:
        dc = (d + c) / 2
        fdc = func(dc)
        if fdc > fc:
            d = dc
            fd = fdc
        else:
            g = c
            fg = fc
            c = dc
            fc = fdc

print(res_list)