Concurrent Gradients Analysis (CGA), and two multi-objective optimization methods based on CGA are provided: Concurrent Gradients Method (CGM), and Pareto Navigator Method (PNM). Dimensionally Independent Response Surface Method (DIRSM) for improving computational efficiency of optimization algorithms is ...
Gradient Flow Algorithm for Unconstrained Optimization无约束最优化问题的梯度流算法 热度: Gradient-based Methods for Optimization Part I基于梯度的优化方法第一部分 热度: an improved wei-yao-liu nonlinear conjugate gradient method for optimization computation:一种改进的渭-尧-非线性共轭梯度法优化计算 ...
Among the three types RADO methods, the latter two are more efficient than the MCS-based one. However, for the design optimization with a large number of design parameters, model-based optimization is time-consuming and sometimes low-fidelity. Moreover, sensitivity-based UQ is inaccurate when th...
We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm with adaptive stepsizes, as well as the AdaGrad algorithm. We provide upper bounds on the regret of both algorithms and show ...
A common feature of many gradient-based methods employed in production optimization is that these gradient approximation techniques have been utilized within the SD framework. The SD framework utilizes first-order information about the objective function to direct the search in the direction of descent ...
FlexiCubesprovides two significant advantages that allow for easy, efficient, and high-quality mesh optimization for various applications: Grad.Differentiation with respect to the mesh is well-defined, and gradient-based optimization converges effectively in practice. ...
In the first talk, we provide a tutorial overview of most of the main approaches currently used for carrying out simulation optimization, which includes stochastic approximation, response surface methodology, and sample average approximation, as well as some random search methods. Simple examples will ...
For a general optimization problem, if the objective function is bounded below and its gradient is Lipschitz continuous, we prove that (a) any trajectory of the gradient-based neural network converges to an equilibrium point, and (b) the Lyapunov stability is equivalent to the asymptotical ...
Lan, G.: Bundle-level methods uniformly optimal for smooth and nonsmooth convex optimization. Math. Program. (2013). doi:10.1007/s10107-013-0737-x Lemarechal, C., Nemirovskii, A., Nesterov, Yu.: New variants of bundle methods. Math. Program. 69, 111–147 (1995) Article MathSciNet ...
The derivation of the analytic gradient, utilized for improving the method performance, is given in subsection 3.1. 3.1. Gradient derivation To apply local search optimization methods, some estimate for the gradient is necessary. In this section, the analytic expression is derived, based on the ...