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Found 6 papers, 1 papers with code
Newton-CG methods for nonconvex unconstrained optimization with Hölder continuous Hessian
In this paper we consider a nonconvex unconstrained optimization problem minimizing a twice differentiable objective function with H\"older continuous Hessian.
Federated Learning with Convex Global and Local Constraints
In practice, many machine learning (ML) problems come with constraints, and their applied domains involve distributed sensitive data that cannot be shared with others, e. g., in healthcare.
A Newton-CG based barrier-augmented Lagrangian method for general nonconvex conic optimization
In this paper we consider finding an approximate second-order stationary point (SOSP) of general nonconvex conic optimization that minimizes a twice differentiable function subject to nonlinear equality constraints and also a convex conic constraint.
A Newton-CG based augmented Lagrangian method for finding a second-order stationary point of nonconvex equality constrained optimization with complexity guarantees
In particular, we first propose a new Newton-CG method for finding an approximate SOSP of unconstrained optimization and show that it enjoys a substantially better complexity than the Newton-CG method [56].
A Newton-CG based barrier method for finding a second-order stationary point of nonconvex conic optimization with complexity guarantees
In this paper we consider finding an approximate second-order stationary point (SOSP) of nonconvex conic optimization that minimizes a twice differentiable function over the intersection of an affine subspace and a convex cone.
Adaptive Trajectory Estimation with Power Limited Steering Model under Perturbation Compensation
The experiment of trajectory estimation demonstrates the convergence of AdaTE, and the better robust to the biased prior statistics and the observation drift compared with EKF, UKF and sparse MAP.
Information Theory Robotics Systems and Control Systems and Control Information Theory G.3.13; J.2.7
