1-d gradient

can i use covlution to get the same result of 1-d gradient

[KSSV]

Read about Taylor Series.

Answer 1

[Jan]

Your question contains the answer already. If the data is a vector, conv is sufficient instead of conv2:

x    = rand(1, 10)
dx_1 = gradient(x)
dx_2 = conv(x, [0.5, 0, -0.5], 'same')

The first and last element of dx_2 differ from dx_1, but you can adjust it with the one-sided quotient of differences easily.

[Yostena Wassef]

input is matrix

so i can't get the same result for 1dgradient

[Jan]

Then the screenshot of the text "When F is a vector" is simply confusing. Please ask the questions as clear as possible.

dx_2 = conv2(x, [0.5, 0, -0.5], 'same')

This replies the same as gradient, except for the first and last column. Then:

dx_2(:, 1)   = diff(x(:, 1:2), 1, 2);
dx_2(:, end) = diff(x(:, end-1:end), 1, 2);

Or if you love conv2:

d = conv2(x, [1, -1], 'valid');
dx_2(:, 1)   = d(:, 1);
dx_2(:, end) = d(:, end);