INTHOP is a second-order method that bounds the difference between an approximate positive definite Hessian and the exact one within an interval, reuses the approximation when iterates stay inside it, and proves global convergence while showing fewer evaluations than steepest descent or quasi-Newton
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INTHOP: A Second-Order Globally Convergent Method for Nonconvex Optimization
INTHOP is a second-order method that bounds the difference between an approximate positive definite Hessian and the exact one within an interval, reuses the approximation when iterates stay inside it, and proves global convergence while showing fewer evaluations than steepest descent or quasi-Newton