Minimization of energy functionals is based on a discretization by the finite element method and optimization by the trust-region method. A key tool is a local evaluation of the approximated gradients together with sparsity of the resulting Hessian matrix. We describe a vectorized MATLAB implementation of the p-Laplace problem in one and two space-dimensions, however it is easily applicable to other energy formulations.
翻译:能源功能的最小化是基于使用有限元素法的离散化和采用信任区域法的优化,一个关键工具是对近似梯度进行局部评价,以及由此形成的赫西安矩阵的宽度,我们描述了在一两个空间二元中将p-laplace问题实施MATLAB的矢量化MATLAB,不过它很容易适用于其他能源配方。