DiOpt: Self-supervised Diffusion for Constrained Optimization
- URL: http://arxiv.org/abs/2502.10330v1
- Date: Fri, 14 Feb 2025 17:43:08 GMT
- Title: DiOpt: Self-supervised Diffusion for Constrained Optimization
- Authors: Shutong Ding, Yimiao Zhou, Ke Hu, Xi Yao, Junchi Yan, Xiaoying Tang, Ye Shi,
- Abstract summary: DiOpt is a novel diffusion paradigm that systematically learns near-optimal feasible solution distributions through iterative self-training.
To our knowledge, DiOpt represents the first successful integration of self-supervised diffusion with hard constraint satisfaction.
- Score: 46.75288477458697
- License:
- Abstract: Recent advances in diffusion models show promising potential for learning-based optimization by leveraging their multimodal sampling capability to escape local optima. However, existing diffusion-based optimization approaches, often reliant on supervised training, lacks a mechanism to ensure strict constraint satisfaction which is often required in real-world applications. One resulting observation is the distributional misalignment, i.e. the generated solution distribution often exhibits small overlap with the feasible domain. In this paper, we propose DiOpt, a novel diffusion paradigm that systematically learns near-optimal feasible solution distributions through iterative self-training. Our framework introduces several key innovations: a target distribution specifically designed to maximize overlap with the constrained solution manifold; a bootstrapped self-training mechanism that adaptively weights candidate solutions based on the severity of constraint violations and optimality gaps; and a dynamic memory buffer that accelerates convergence by retaining high-quality solutions over training iterations. To our knowledge, DiOpt represents the first successful integration of self-supervised diffusion with hard constraint satisfaction. Evaluations on diverse tasks, including power grid control, motion retargeting, wireless allocation demonstrate its superiority in terms of both optimality and constraint satisfaction.
Related papers
- Test-time Alignment of Diffusion Models without Reward Over-optimization [8.981605934618349]
Diffusion models excel in generative tasks, but aligning them with specific objectives remains challenging.
We propose a training-free, test-time method based on Sequential Monte Carlo (SMC) to sample from the reward-aligned target distribution.
We demonstrate its effectiveness in single-reward optimization, multi-objective scenarios, and online black-box optimization.
arXiv Detail & Related papers (2025-01-10T09:10:30Z) - Diffusion Models as Network Optimizers: Explorations and Analysis [71.69869025878856]
generative diffusion models (GDMs) have emerged as a promising new approach to network optimization.
In this study, we first explore the intrinsic characteristics of generative models.
We provide a concise theoretical and intuitive demonstration of the advantages of generative models over discriminative network optimization.
arXiv Detail & Related papers (2024-11-01T09:05:47Z) - DiffSG: A Generative Solver for Network Optimization with Diffusion Model [75.27274046562806]
Diffusion generative models can consider a broader range of solutions and exhibit stronger generalization by learning parameters.
We propose a new framework, which leverages intrinsic distribution learning of diffusion generative models to learn high-quality solutions.
arXiv Detail & Related papers (2024-08-13T07:56:21Z) - HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection [66.42229859018775]
We introduce a unified, high-capacity weakly supervised object detection (WSOD) network called HUWSOD.
HUWSOD incorporates a self-supervised proposal generator and an autoencoder proposal generator with a multi-rate re-supervised pyramid to replace traditional object proposals.
Our findings indicate that randomly boxes, although significantly different from well-designed offline object proposals, are effective for WSOD training.
arXiv Detail & Related papers (2024-06-27T17:59:49Z) - Efficient Text-driven Motion Generation via Latent Consistency Training [21.348658259929053]
We propose a motion latent consistency training framework (MLCT) to solve nonlinear reverse diffusion trajectories.
By combining these enhancements, we achieve stable and consistency training in non-pixel modality and latent representation spaces.
arXiv Detail & Related papers (2024-05-05T02:11:57Z) - Quantization Avoids Saddle Points in Distributed Optimization [1.579622195923387]
Distributed non optimization underpins key functionalities of numerous distributed systems.
The aim of this paper is to prove that it can effectively escape saddle points convergence to a second-order stationary point convergence.
With an easily adjustable quantization, the approach allows a user control to aggressively reduce communication overhead.
arXiv Detail & Related papers (2024-03-15T15:58:20Z) - Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
We argue for the use of neural generative models to characterize the worst-case distribution.
This approach poses a number of implementation and optimization challenges.
We find that the proposed approach yields models that are more robust than comparable baselines.
arXiv Detail & Related papers (2021-03-18T14:26:26Z) - Online Optimization and Ambiguity-based Learning of Distributionally Uncertain Dynamic Systems [1.6709415233613623]
This paper proposes a novel approach to construct data-driven online solutions to optimization problems (P) subject to a class of distributionally uncertain dynamical systems.
The introduced framework allows for the simultaneous learning of distributional system uncertainty via a parameterized, control-dependent ambiguity set.
We also introduce an online version of Nesterov's accelerated-gradient algorithm, and analyze its performance to solve this class of problems via dissipativity theory.
arXiv Detail & Related papers (2021-02-18T01:49:06Z) - Combining Deep Learning and Optimization for Security-Constrained
Optimal Power Flow [94.24763814458686]
Security-constrained optimal power flow (SCOPF) is fundamental in power systems.
Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs.
This paper proposes a novel approach that combines deep learning and robust optimization techniques.
arXiv Detail & Related papers (2020-07-14T12:38:21Z)
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.