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Interior point methods solve constrained optimization by replacing hard constraints with a smooth barrier that becomes infinite at the boundary, keeping iterates strictly inside the feasible region.
The Maximum Entropy Principle picks the probability distribution with the greatest uncertainty (entropy) that still satisfies the facts you know (constraints).