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Estimating Multi-chirp Parameters using Curvature-guided Langevin Monte Carlo

arXiv 2501.18178 ↗First posted 30 Jan 2025eess.SP cs.LG stat.MLcited by 0

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Abstract (arXiv)

This paper considers the problem of estimating chirp parameters from a noisy mixture of chirps. While a rich body of work exists in this area, challenges remain when extending these techniques to chirps of higher order polynomials. We formulate this as a non-convex optimization problem and propose a modified Langevin Monte Carlo (LMC) sampler that exploits the average curvature of the objective function to reliably find the minimizer. Results show that our Curvature-guided LMC (CG-LMC) algorithm is robust and succeeds even in low SNR regimes, making it viable for practical applications.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 37 references that are in the map. First posted on arXiv 30 Jan 2025.

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References · 10

  1. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  2. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  3. 2017Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysiscited by 242
  4. 2017Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimizationcited by 96
  5. 2018Sampling Can Be Faster Than Optimizationcited by 68
  6. 2017Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlocited by 33
  7. 2019Langevin Monte Carlo without smoothnesscited by 27
  8. 2021A Survey of Monte Carlo Methods for Parameter Estimationcited by 23
  9. 2022Single Loop Gaussian Homotopy Method for Non-convex Optimizationcited by 15
  10. 2021Hessian Estimation via Stein's Identity in Black-Box Problemscited by 8

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