In this paper we study consensus-based optimization (CBO), which is a multiagent
metaheuristic derivative-free optimization method that can globally minimize nonconvex …

Sampling approaches like Markov chain Monte Carlo were once popular for combinatorial
optimization, but the inefficiency of classical methods and the need for problem-specific …

Nature-inspired swarm-based algorithms are increasingly applied to tackle high-
dimensional and complex optimization problems across disciplines. They are general …

Anderson mixing (AM) is an acceleration method for fixed-point iterations. Despite its
success and wide usage in scientific computing, the convergence theory of AM remains …

We study the temperature control problem for Langevin diffusions in the context of
nonconvex optimization. The classical optimal control of such a problem is of the bang-bang …

This paper considers time-inconsistent stop** problems in which the inconsistency arises
from a class of nonexponential discount functions called weighted discount functions. We …

In this paper, we present a novel maximum entropy formulation of the Differential Dynamic
Programming algorithm and derive two variants using unimodal and multimodal value …

Stochastic gradient Langevin dynamics (SGLD) is a fundamental algorithm in stochastic
optimization. Recent work by Zhang et al.(2017) presents an analysis for the hitting time of …

We present a novel second-order trajectory optimization algorithm based on Stein
Variational Newton's Method and Maximum Entropy Differential Dynamic Programming. The …

Principal component analysis (PCA) is a simple and popular tool for processing high-
dimensional data. We investigate its effectiveness for matrix denoising. We assume iid high …