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  • Articles  (8)
  • Nonlinear programming  (8)
  • 1975-1979  (8)
  • 1945-1949
  • 1977  (8)
  • Mathematics  (8)
  • Sociology
  • Architecture, Civil Engineering, Surveying
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  • Articles  (8)
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Years
  • 1975-1979  (8)
  • 1945-1949
Year
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  • Mathematics  (8)
  • Sociology
  • Architecture, Civil Engineering, Surveying
  • Computer Science  (3)
  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 12 (1977), S. 281-284 
    ISSN: 1436-4646
    Keywords: Nonlinear programming ; Augmented Lagrangian functions ; Sensitivity analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 13 (1977), S. 140-155 
    ISSN: 1436-4646
    Keywords: Minimax optimization ; Nonlinear programming ; Computer-aided network design
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A constrained minimax problem is converted to minimization of a sequence of unconstrained and continuously differentiable functions in a manner similar to Morrison's method for constrained optimization. One can thus apply any efficient gradient minimization technique to do the unconstrained minimization at each step of the sequence. Based on this approach, two algorithms are proposed, where the first one is simpler to program, and the second one is faster in general. To show the efficiency of the algorithms even for unconstrained problems, examples are taken to compare the two algorithms with recent methods in the literature. It is found that the second algorithm converges faster with respect to the other methods. Several constrained examples are also tried and the results are presented.
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 13 (1977), S. 49-68 
    ISSN: 1436-4646
    Keywords: Algorithm ; APL-code ; Barycentric representation ; Decomposition ; Nonlinear programming ; Pseudo-concave objective ; Simplex
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Simplicial decomposition is a special version of the Dantzig—Wolfe decomposition principle, based on Carathéodory's theorem. The associated class of algorithms has the following features and advantages: The master and the subprogram are constructed without dual variables; the methods remain therefore well-defined for non-concave objective functions, and pseudo-concavity suffices for convergence to global maxima. The subprogram produces affinely independent sets of feasible generator points defining simplices, which the master program keeps minimal by dropping redundant generator points and finding maximizers in the relative interiors of the resulting subsimplices. The use of parallel subspaces allows the direct application of any unrestricted optimization method in the master program; thus the best unconstrained procedure for any type of objective function can be used to find constrained maximizers for it. The paper presents the theory for this class of algorithms, the APL-code of a “demonstration” method and some computational experience with Colville's test problems.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 21 (1977), S. 121-135 
    ISSN: 1573-2878
    Keywords: Nonlinear programming ; max-min problems ; Lagrange multiplier technique ; Newton's method
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Our aim here is to present numerical methods for solving a general nonlinear programming problem. These methods are based on transformation of a given constrained minimization problem into an unconstrained maximin problem. This transformation is done by using a generalized Lagrange multiplier technique. Such an approach permits us to use Newton's and gradient methods for nonlinear programming. Convergence proofs are provided, and some numerical results are given.
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 21 (1977), S. 251-259 
    ISSN: 1573-2878
    Keywords: Nonlinear programming ; variable-metric methods ; parameter optimization ; function minimization ; mathematical programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Variable-metric methods are presented which do not need an accurate one-dimensional search and eliminate roundoff error problems which can occur in updating the metric for large-dimension systems. The methods are based on updating the square root of the metric, so that a positive-definite metric always results. The disadvantage of intentionally relaxing the accuracy of the one-dimensional search is that the number of iterations (and hence, gradient evaluations) increases. For problems involving a large number of variables, the square-root method is presented in a triangular form to reduce the amount of computation. Also, for usual optimization problems, the square-root procedure can be carried out entirely in terms of the metric, eliminating storage and computer time associated with computations of the square root of the metric.
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 21 (1977), S. 235-239 
    ISSN: 1573-2878
    Keywords: Nonlinear programming ; computing methods ; Lagrange multiplier estimates ; bounded variables ; large-scale problems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Some recent methods for solving nonlinear programming problems make use of estimates of the Lagrange multipliers. These estimates are usually calculated by solving a system oft linear equations, wheret is the number of active constraints. It is shown that, when a large proportion of the active constraints consists of simple upper or lower bounds on the variables, then computational effort can be saved by means of a reorganization of this linear system.
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 22 (1977), S. 297-309 
    ISSN: 1573-2878
    Keywords: Nonlinear programming ; global convergence ; exact penalty function
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract Recently developed Newton and quasi-Newton methods for nonlinear programming possess only local convergence properties. Adopting the concept of the damped Newton method in unconstrained optimization, we propose a stepsize procedure to maintain the monotone decrease of an exact penalty function. In so doing, the convergence of the method is globalized.
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 23 (1977), S. 471-471 
    ISSN: 1573-2878
    Keywords: Nonlinear programming ; unidemensional search ; numerical methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A correction of the procedure of Ref. 1 is given.
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