Latest experiences in improving convergence of dual optimization in LR method

Xiaoming Feng, Yuan Liao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper presents latest application experiences in dual optimization performance in Lagrangian Relaxation (LR) Method for solving power system resource scheduling problems. LR is widely used in solving a common class of optimization problems where the removal of the coupling constraints results in a collection of subproblems that can be independently and efficiently solved. The overall efficiency of the LR method is predominately determined by the computational efficiency of the dual optimization procedure, i.e. the number of iterations and the computational effort in each iteration. Although several alternatives have been developed, most of which can achieve eventual convergence in theory, no solution approach has been found to produce consistent and satisfactory convergence performance in practice. The most common approach for dual problem optimization, the subgradient method, used by many practitioners for its simplicity and low computational overhead, has been reported to suffer from slow convergence or premature stall. This paper presents encouraging experiences in achieving speedy convergence by judicious determination of the step size scaling factor based on simple rules that can be easily codified.

Original languageEnglish
Title of host publication2005 IEEE Russia Power Tech, PowerTech
StatePublished - 2005
Event2005 IEEE Russia Power Tech, PowerTech - St. Petersburg, Russian Federation
Duration: Jun 27 2005Jun 30 2005

Publication series

Name2005 IEEE Russia Power Tech, PowerTech


Conference2005 IEEE Russia Power Tech, PowerTech
Country/TerritoryRussian Federation
CitySt. Petersburg


  • Dual optimization
  • Lagrangian relaxation method
  • Resource scheduling problem
  • Subgradient method

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering


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