Gradient Projection Network: Analog Solver for Linearly Constrained Nonlinear Programming

Kiichi Urahama

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


An analog approach is presented for solving nonlinear programming problems with linear constraint conditions. The present method is based on transformation of variables with exponential functions, which enables every trajectory to pass through an interior of feasible regions along a gradient direction projected onto the feasible space. Convergence of its trajectory to the solution of optimization problems is guaranteed and it is shown that the present scheme is an extension of the affine scaling method for linear programming to nonlinear programs under a slight modification of Riemannian metric. An analog electronic circuit is also presented that implements the proposed scheme in real time.

Original languageEnglish
Pages (from-to)1061-1073
Number of pages13
JournalNeural Computation
Issue number5
Publication statusPublished - Jul 1 1996
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience


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