An Element-wise RSAV Algorithm for Unconstrained Optimization Problems
- URL: http://arxiv.org/abs/2309.04013v1
- Date: Thu, 7 Sep 2023 20:37:23 GMT
- Title: An Element-wise RSAV Algorithm for Unconstrained Optimization Problems
- Authors: Shiheng Zhang, Jiahao Zhang, Jie Shen and Guang Lin
- Abstract summary: We present a novel optimization algorithm, element-wise relaxed scalar auxiliary variable (E-RSAV)
Our algorithm features rigorous proofs of linear convergence in the convex setting.
We also propose an adaptive version of E-RSAV with Steffensen step size.
- Score: 13.975774245256561
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a novel optimization algorithm, element-wise relaxed scalar
auxiliary variable (E-RSAV), that satisfies an unconditional energy dissipation
law and exhibits improved alignment between the modified and the original
energy. Our algorithm features rigorous proofs of linear convergence in the
convex setting. Furthermore, we present a simple accelerated algorithm that
improves the linear convergence rate to super-linear in the univariate case. We
also propose an adaptive version of E-RSAV with Steffensen step size. We
validate the robustness and fast convergence of our algorithm through ample
numerical experiments.
Related papers
- Faster WIND: Accelerating Iterative Best-of-$N$ Distillation for LLM Alignment [81.84950252537618]
This paper reveals a unified game-theoretic connection between iterative BOND and self-play alignment.
We establish a novel framework, WIN rate Dominance (WIND), with a series of efficient algorithms for regularized win rate dominance optimization.
arXiv Detail & Related papers (2024-10-28T04:47:39Z) - An Accelerated Block Proximal Framework with Adaptive Momentum for
Nonconvex and Nonsmooth Optimization [2.323238724742687]
We propose an accelerated block proximal linear framework with adaptive momentum (ABPL$+$) for nonsmooth and nonsmooth optimization.
We analyze the potential causes of the extrapolation step failing in some algorithms, and resolve this issue by enhancing the comparison process.
We extend our algorithm to any scenario involving updating the gradient step and the linear extrapolation step.
arXiv Detail & Related papers (2023-08-23T13:32:31Z) - Linearization Algorithms for Fully Composite Optimization [61.20539085730636]
This paper studies first-order algorithms for solving fully composite optimization problems convex compact sets.
We leverage the structure of the objective by handling differentiable and non-differentiable separately, linearizing only the smooth parts.
arXiv Detail & Related papers (2023-02-24T18:41:48Z) - Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods [75.34939761152587]
Efficient computation of the optimal transport distance between two distributions serves as an algorithm that empowers various applications.
This paper develops a scalable first-order optimization-based method that computes optimal transport to within $varepsilon$ additive accuracy.
arXiv Detail & Related papers (2023-01-30T15:46:39Z) - Adaptive Stochastic Optimisation of Nonconvex Composite Objectives [2.1700203922407493]
We propose and analyse a family of generalised composite mirror descent algorithms.
With adaptive step sizes, the proposed algorithms converge without requiring prior knowledge of the problem.
We exploit the low-dimensional structure of the decision sets for high-dimensional problems.
arXiv Detail & Related papers (2022-11-21T18:31:43Z) - An Adaptive Gradient Method with Energy and Momentum [0.0]
We introduce a novel algorithm for gradient-based optimization of objective functions.
The method is simple to implement, computationally efficient, and well suited for large-scale machine learning problems.
arXiv Detail & Related papers (2022-03-23T04:48:38Z) - Distributed Proximal Splitting Algorithms with Rates and Acceleration [7.691755449724637]
We derive sublinear and linear convergence results with new rates on the function value suboptimality or distance to the solution.
We propose distributed variants of these algorithms, which can be accelerated as well.
arXiv Detail & Related papers (2020-10-02T12:35:09Z) - Convergence of adaptive algorithms for weakly convex constrained
optimization [59.36386973876765]
We prove the $mathcaltilde O(t-1/4)$ rate of convergence for the norm of the gradient of Moreau envelope.
Our analysis works with mini-batch size of $1$, constant first and second order moment parameters, and possibly smooth optimization domains.
arXiv Detail & Related papers (2020-06-11T17:43:19Z) - Effective Dimension Adaptive Sketching Methods for Faster Regularized
Least-Squares Optimization [56.05635751529922]
We propose a new randomized algorithm for solving L2-regularized least-squares problems based on sketching.
We consider two of the most popular random embeddings, namely, Gaussian embeddings and the Subsampled Randomized Hadamard Transform (SRHT)
arXiv Detail & Related papers (2020-06-10T15:00:09Z) - Optimal Randomized First-Order Methods for Least-Squares Problems [56.05635751529922]
This class of algorithms encompasses several randomized methods among the fastest solvers for least-squares problems.
We focus on two classical embeddings, namely, Gaussian projections and subsampled Hadamard transforms.
Our resulting algorithm yields the best complexity known for solving least-squares problems with no condition number dependence.
arXiv Detail & Related papers (2020-02-21T17:45:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.