A New Continuous-Time Equality-Constrained Optimization to Avoid Singularity

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Abstract

In equality-constrained optimization, a standard regularity assumption is often associated with feasible point methods, namely, that the gradients of constraints are linearly independent. In practice, the regularity assumption may be violated. In order to avoid such a singularity, a new projection matrix is proposed based on which a feasible point method to continuous-Time, equality-constrained optimization is developed. First, the equality constraint is transformed into a continuous-Time dynamical system with solutions that always satisfy the equality constraint. Second, a new projection matrix without singularity is proposed to realize the transformation. An update (or say a controller) is subsequently designed to decrease the objective function along the solutions of the transformed continuous-Time dynamical system. The invariance principle is then applied to analyze the behavior of the solution. Furthermore, the proposed method is modified to address cases in which solutions do not satisfy the equality constraint. Finally, the proposed optimization approach is applied to three examples to demonstrate its effectiveness.

Original languageEnglish
Article number7273919
Pages (from-to)262-272
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume27
Issue number2
DOIs
StatePublished - Feb 2016

Keywords

  • Continuous-Time dynamical systems
  • equality constraints
  • optimization
  • singularity.

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