SecondDerivative2012.pdf
web.mit.edu · 5,012 words · saved by 1 readers
N/A
Numerical Computation of Second Derivatives 1 with Applications to Optimization Problems Philip Caplan – pcaplan@mit.edu Abstract Newton’s method is applied to the minimization of a computationally expensive objective function. Various methods for computing the exact Hessian are examined, notably adjoint-based methods and the hyper-dual method. The hyper-dual number method still…
related reading
- Universal Complexity Bounds for Universal Gradient Methods in Nonlinear Optimizationarxiv.org
- [1907.03452] Deep splitting method for parabolic PDEsarxiv.org
- Why Momentum Really Worksdistill.pub
- prox_algs.pdfweb.stanford.edu
- [2605.15522] Stochastic Non-Smooth Convex Optimization with Unbounded Gradientsarxiv.org
- Differentiable Programming from Scratchthenumb.at
- num_pde_fub.pdfwias-berlin.de
- [1605.08101] Global rates of convergence for nonconvex optimization on manifoldsarxiv.org
- Fast Simulation of Mass-Spring Systemsusers.cs.utah.edu
- Closed-form dynamics beyond quadratics – Machine Learning Research Blogfrancisbach.com
- Hamilton-Jacobi Equationsmath.mcgill.ca
- Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learningarxiv.org