CONMIN is a FORTRAN program, in subroutine form, for the solution of linear or nonlinear constrained optimization problems. The basic optimization algorithm is the Method of Feasible Directions. The user must provide a main calling program and an external routine to evaluate the objective and constraint functions and to provide gradient information. If analytic gradients of the objective or constraint functions are not available, this information is calculated by finite difference. While the program is intended primarily for efficient solution of constrained problems, unconstrained function minimization problems may also be solved, and the conjugate direction method of Fletcher and Reeves is used for this purpose. This manual describes the use of CONMIN and defines all necessary parameters. Sufficient information is provided so that the program can be used without special knowledge of optimization techniques. Sample problems are inc! luded to help the user become familiar with CONMIN and to make the program operational.

References in zbMATH (referenced in 51 articles )

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  1. Andrei, Neculai: Accelerated memory-less SR1 method with generalized secant equation for unconstrained optimization (2022)
  2. Andrei, Neculai: Accelerated adaptive Perry conjugate gradient algorithms based on the self-scaling memoryless BFGS update (2017)
  3. Saka, Mehmet Polat; Geem, Zong Woo: Mathematical and metaheuristic applications in design optimization of steel frame structures: an extensive review (2013)
  4. Heydari, Masoud; Moharrami, Hamid; Yazdani-Paraei, Hadi: Nonlinear analysis and optimum design of Guyed masts (2012)
  5. Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R. R. A.: PyOpt: a python-based object-oriented framework for nonlinear constrained optimization (2012)
  6. Andrei, Neculai: New accelerated conjugate gradient algorithms as a modification of Dai-Yuan’s computational scheme for unconstrained optimization (2010)
  7. Andrei, Neculai: Accelerated conjugate gradient algorithm with finite difference Hessian/vector product approximation for unconstrained optimization (2009)
  8. Kim, Jong-Eun; Rao, Vinay N.; Koomullil, Roy P.; Ross, Doug H.; Soni, Bharat K.; Shih, Alan M.: Development of an efficient aerodynamic shape optimization framework (2009)
  9. Andrei, Neculai: A scaled nonlinear conjugate gradient algorithm for unconstrained optimization (2008)
  10. Tanskanen, Pasi: A multiobjective and fixed elements based modification of the evolutionary structural optimization method (2006)
  11. Le Pape, A.; Beaumier, P.: Numerical optimization of helicopter rotor aerodynamic performance in hover (2005)
  12. Sedaghati, R.: Benchmark case studies in structural design optimization using the force method (2005)
  13. Burguburu, Stéphane; le Pape, Arnaud: Improved aerodynamic design of turbomachinery bladings by numerical optimization. (2003)
  14. Sedaghati, R.; Esmailzadeh, E.: Optimum design of structures with stress and displacement constraints using the force method (2003)
  15. Shen, Jie; Yoon, David: A new scheme for efficient and direct shape optimization of complex structures represented by polygonal meshes (2003)
  16. Phua, Paul Kang-Hoh; Ming, Daohua; Fan, Weiguo; Zhang, Yan: Parallel algorithms for solving large-scale nonlinear optimization problems. (2001)
  17. Borlase, George A.; Vlahopoulos, Nickolas: An energy finite element optimization process for reducing high-frequency vibration in large-scale structures. (2000)
  18. Sistla, R.; Dovi, A. R.; Su, P.: A distributed, heterogeneous computing environment for multidisciplinary design and analysis of aerospace vehicles (2000)
  19. Holstad, Astrid: Numerical solution of nonlinear equations in chemical speciation calculations (1999)
  20. Song, X.; Baldwin, J. D.: A novel node-based structural shape optimization algorithm (1999)

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