2 added 57 characters in body

Convex optimization problem:

Minimize

The goal is to minimize,

$\sum{|y-x|^2}$,

such that,

a] K-L divergence between probability distributions for x and y, is maximum i.e. D(p(y)||q(x)) is maximum

b] $\sum{y} < \alpha$, where $\alpha$ is constant.

c] x,y < $\beta$

How can I approach this problem?

1

# convex optimization problem

Convex optimization problem:

Minimize, $\sum{|y-x|^2}$,

such that,
a] K-L divergence between probability distributions for x and y, is maximum i.e. D(p(y)||q(x)) is maximum

b] $\sum{y} < \alpha$, where $\alpha$ is constant.

c] x,y < $\beta$