- #1
ahmadnajeeb
- 1
- 0
Hi,
I want to know the solution of the following equation.
<br /> a = argmin_{a}[\sum{||a^Tx_i - y_i||^2}+\alpha ||a||^2] \\<br />
where x_i, y_i are column vectors of dimensions m and n respectively where m>n. \alpha is a scalar and
Y = a^T X where X=[x_1 x_2 ... x_k], Y = [y_1 y_2 ... y_k]
I know that without this constraint \alpha ||a||^2, its a simple least square optimization problem and I can solve it using Matlab's inverse operator. I want to use the same inverse operator but don't know how this constraint changes my original model.
I want to know the solution of the following equation.
<br /> a = argmin_{a}[\sum{||a^Tx_i - y_i||^2}+\alpha ||a||^2] \\<br />
where x_i, y_i are column vectors of dimensions m and n respectively where m>n. \alpha is a scalar and
Y = a^T X where X=[x_1 x_2 ... x_k], Y = [y_1 y_2 ... y_k]
I know that without this constraint \alpha ||a||^2, its a simple least square optimization problem and I can solve it using Matlab's inverse operator. I want to use the same inverse operator but don't know how this constraint changes my original model.