It is simple, I know but I have little understanding of convex optimization yet Problem definition: Objective function is II b - Aw II norm 2 a vector of unknown [w1, w2, ..., wn] a data matrix A (m x n), each row has n components([ai1, ai2, ..., ain]), m...

I have an optimization problem that has in the objective function 2 multiplied variables, making the model quadratic. I am currently using zimpl, to parse the model, and glpk to solve it. As they don't support quadratic programming, I would need to convert this to an MILP. . The first...

I have a vector A of length N. Also I have N*N matrix C. I want to maximize following equation : minimize (- (w_transpose * A) + p * w_transpose * C * w) Where w is a vector of length N, with constraints that each w is non-negative and...