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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? |
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convex optimization problemConvex optimization problem: Minimize, $\sum{|y-x|^2}$, such that, b] $\sum{y} < \alpha$, where $\alpha$ is constant. c] x,y < $\beta$
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