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...
Optimization Methods and SoftwareFischer 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...
We propose a semismooth Newton-type method for nonsmooth optimal control problems. Its particular feature is the combination of a quasi-Newton method with
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...
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, ...
Method 2 Li and Yu [64] introduced a method for the global optimization of non-linear mathematical models in which the objective along with the constraint sets may be non-convex. First, a univariate mathematical function is formulated via a piecewise linear function using a sum of absolute expr...
Newton’s method and its use in optimization European Journal of Operational Research (2007) A. Berman et al. Nonnegative matrices in the mathematical sciences (1979) O. Bokanowski et al. Some convergence results for Howard’s algorithm SIAM Journal on Numerical Analysis (2009) L. Brugnano et...
3.3 Quasi-Newton (BFGS) 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...
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 ...