12 papers
Stochastic convergence of parallel asynchronous adaptive first-order methods
Serge Gratton, Philippe L. Toint
A new class of asynchronous adaptive first-order optimization methods is introduced, comprising asynchronous variants of several popular algorithms. Versions of these methods using…
A unified convergence theory for adaptive first-order methods in the nonconvex case, including AdaNorm, full and diagonal AdaGrad, Shampoo and Muo
S. Gratton, Ph. L. Toint
A unified framework for first-order optimization algorithms fornonconvex unconstrained optimization is proposed that uses adaptivelypreconditioned gradients and includes popular me…
An objective-function-free algorithm for nonconvex stochastic optimization with deterministic equality and inequality constraints
S. Gratton, Ph. L. Toint
An algorithm is proposed for solving optimization problems with stochastic objective and deterministic equality and inequality constraints. This algorithm is objective-function-fre…
A Simple First-Order Algorithm for Full-Rank Equality Constrained Optimization
Serge Gratton, Philippe L. Toint
A very simple first-order algorithm is proposed for solving nonlinear optimization problems with deterministic nonlinear equality constraints. This algorithm adaptively selects ste…
A Fast Newton Method Under Local Lipschitz Smoothness
Serge Gratton, Sadok Jerad, Philippe L. Toint
A new, fast second-order method is proposed that achieves the optimal complexity to obtain first-order -stationary points. Crucial…
An objective-function-free algorithm for general smooth constrained optimization
S. Bellavia, S. Gratton, B. Morini +1
A new algorithm for smooth constrained optimization is proposed that never computes the value of the problem's objective function and that handles both equality and inequality cons…