A special newton-type optimization method: Optimization: Vol 24, No 3-4doi:10.1080/02331939208843795Nonlinear Programming AlgorithmsInequality ConstraintsKuhn-Tucker PointsNewton’s MethodClarke’s JacobianStrict Complementary Slackness ConditionA. FischerAbteilung MathematikOptimization
Fischer, A.: A special Newton-type optimization method. Optimization 24, 269–284 (1992). https://doi.org/10.1080/02331939208843795 Article MathSciNet MATH Google Scholar Frank, M., Wolfe, P.: An algorithm for quadratic programming. Naval Res. Logist. Q. 3(1–2), 95–110 (1956). ht...
Fischer A. On the local superlinear convergence of a Newton-type method for LCP under weak condition [J]. Optim Method and Software, 1995, 6:83 -107.On the local superlinear convergence of a Newton-type method for LCP under weak conditions, Optimization Methods and Software 6 - Fischer - ...
The cost function, as described by Equation (7), was minimized using the Broyden-Fletcher-Goldfarb-Shanno algorithm, a quasi-Newton optimization method implemented in the MTP framework. Fundamentally, training the MTP model with atomic configurations entails finding the parameter set {Θ} by solving...
We propose a semismooth Newton-type method for nonsmooth optimal control problems. Its particular feature is the combination of a quasi-Newton method with
A Diagonal-Sparse Quasi-Newton Method for Unconstrained Optimization Problem无约束优化问题的对角稀疏拟牛顿法对角稀疏拟牛顿法非精确搜索全局收敛性收敛速度In this paper, we present a diagonal-sparse quasi-Newton method for unconstrained optimization problems. The method is similar to quasi-Newton method, ...
We see the Hessian matrix used in large-scale optimization problems using Newton-type methods because they are the coefficients of the quadratic term of a local Taylor expansion. In practice, the Hessian can be computationally difficult to compute. We tend to see quasi-Newton algorithms used ...
BFGS is a type of quasi-Newton method. It seeks to approximate the inverse of the Hessian using the function’s gradient information. This approximation is such that it does not involve second derivatives. Thus, this method has a slower convergence rate than Newton’s methods, although it is...
For the Lagrangian-DNN relaxation of quadratic optimization problems (QOPs), we propose a Newton-bracketing method to improve the performance of the bisection-projection method implemented in BBCPOP [to appear in ACM Tran. Softw., 2019]. The relaxation problem is converted into the problem of ...
Smooth convex optimizationproximal-Newton methodcomplexityproximal point methodsWe propose and study the iteration-complexity of a proximal-Newton method for finding approximate solutions of the problem of minimizing a twice continuously differentiable convex function on a (possibly infinite dimensional) ...