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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.
Newton's method uses both the gradient and the Hessian to take steps that aim directly at the local optimum by fitting a quadratic model of the loss around the current point.