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他是一位优化算法领域的专家,曾获得过高等教育国家杰出教师奖、欧洲数学学会奖等。他的代表作品包括了《Introductory Lectures on Convex Optimization》、《Convex Optimization》等。 四、数学原理 Nesterov加速梯度法是一种改进的梯度下降算法。它的核心思想是,在每次迭代中,先沿着原来的梯度方向走一步,再沿着估计的下...
In his algorithms, Nesterov makes explicit use of a Lipschitz constant L for the function gradient, which is either assumed known (Nesterov in Introductory lectures on convex optimization. A basic course. Kluwer, Boston, 2004), or is estimated by an adaptive procedure (Nesterov 2007). We ...
Betancourt, M., Jordan, M.I., Ashia C.W.: On Symplectic Optimization (2018) Bof, N., Carli, R., Schenato, L.: Lyapunov Theory for Discrete Time Systems, (2018) Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, USA (2004) Book MATH Google Scholar Bravetti...
Nesterov, Introductory Lectures on Convex Optimization: A basic course, Kluwer Academic Publishers, Massachusetts, 2004.] to compute the ground state of nonlinear Schrodinger equations, which can potentially include a fractional laplacian term. A comparison is developed with standard gradient flow ...