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Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints

arXiv 2305.04433 ↗First posted 8 May 2023math.OC cs.LGcited by 0

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Abstract (arXiv)

Convex optimization with equality and inequality constraints is a ubiquitous problem in several optimization and control problems in large-scale systems. Recently there has been a lot of interest in establishing accelerated convergence of the loss function. A class of high-order tuners was recently proposed in an effort to lead to accelerated convergence for the case when no constraints are present. In this paper, we propose a new high-order tuner that can accommodate the presence of equality constraints. In order to accommodate the underlying box constraints, time-varying gains are introduced in the high-order tuner which leverage convexity and ensure anytime feasibility of the constraints. Numerical examples are provided to support the theoretical derivations.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 8 May 2023.

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References · 10

  1. 2021Convex optimizationcited by 2,479
  2. 2015A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insightscited by 203
  3. 2021DC3: A learning method for optimization with hard constraintscited by 181
  4. 2020Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networkscited by 96
  5. 2019DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flowcited by 50
  6. 2019DeepOPF: Deep Neural Network for DC Optimal Power Flowcited by 32
  7. 2016Accelerated first-order primal-dual proximal methods for linearly constrained composite convex programmingcited by 6
  8. 2020A Stable High-order Tuner for General Convex Functionscited by 4
  9. 2021Nesterov Acceleration for Equality-Constrained Convex Optimization via Continuously Differentiable Penalty Functionscited by 1
  10. 2022Accelerated Algorithms for a Class of Optimization Problems with Constraintscited by 1

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