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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.
ADMM splits a hard optimization problem into two easier subproblems that communicate through simple averaging-like steps.