A deterministic annealing neural network for convex programming

Jun Wang

Research output: Contribution to journalArticlepeer-review

141 Citations (Scopus)


A recurrent neural network, called a deterministic annealing neural network, is proposed for solving convex programming problems. The proposed deterministic annealing neural network is shown to be capable of generating optimal solutions to convex programming problems. The conditions for asymptotic stability, solution feasibility, and solution optimality are derived. The design methodology for determining design parameters is discussed. Three detailed illustrative examples are also presented to demonstrate the functional and operational characteristics of the deterministic annealing neural network in solving linear and quadratic programs.

Original languageEnglish
Pages (from-to)629-641
Number of pages13
JournalNeural Networks
Issue number4
Publication statusPublished - 1994
Externally publishedYes


  • Convergence analysis
  • Convex programming
  • Recurrent neural network


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