An optimized dynamic mode decomposition model robust to multiplicative noise
Dynamic mode decomposition (DMD) is an efficient tool for decomposing spatio-temporal
data into a set of low-dimensional modes, yielding the oscillation frequencies and the growth …
data into a set of low-dimensional modes, yielding the oscillation frequencies and the growth …
Efficient and provably convergent randomized greedy algorithms for neural network optimization
Greedy algorithms have been successfully analyzed and applied in training neural networks
for solving variational problems, ensuring guaranteed convergence orders. However, their …
for solving variational problems, ensuring guaranteed convergence orders. However, their …
Subspace correction methods for semicoercive and nearly semicoercive convex optimization with applications to nonlinear PDEs
We present new convergence analyses for subspace correction methods for semicoercive
and nearly semicoercive convex optimization problems, generalizing the theory of singular …
and nearly semicoercive convex optimization problems, generalizing the theory of singular …
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime
We propose a new training algorithm, named DualFL (Dualized Federated Learning), for
solving distributed optimization problems in federated learning. DualFL achieves …
solving distributed optimization problems in federated learning. DualFL achieves …