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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 16 (1975), S. 429-445 
    ISSN: 1573-2878
    Keywords: Mathematical programming ; conjugate-gradient methods ; variable-metric methods ; linear equations ; numerical methods ; computing methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract A computationally stable method for the general solution of a system of linear equations is given. The system isA Tx−B=0, where then-vectorx is unknown and then×q matrixA and theq-vectorB are known. It is assumed that the matrixA T and the augmented matrix [A T,B] are of the same rankm, wherem≤n, so that the system is consistent and solvable. Whenm〈n, the method yields the minimum modulus solutionx m and a symmetricn ×n matrixH m of rankn−m, so thatx=x m+H my satisfies the system for ally, ann-vector. Whenm=n, the matrixH m reduces to zero andx m becomes the unique solution of the system. The method is also suitable for the solution of a determined system ofn linear equations. When then×n coefficient matrix is ill-conditioned, the method can produce a good solution, while the commonly used elimination method fails.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 16 (1975), S. 447-485 
    ISSN: 1573-2878
    Keywords: Nonlinear programming ; mathematical programming ; quadratically convergent algorithms ; conjugate-gradient methods ; variable-metric methods ; computing methods ; numerical methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract The problem of minimizing a functionf(x) subject to the constraint ϕ(x)=0 is considered. Here,f is a scalar,x is ann-vector, and ϕ is anm-vector, wherem 〈n. A general quadratically convergent algorithm is presented. The conjugate-gradient algorithm and the variable-metric algorithms for constrained function minimization can be obtained as particular cases of the general algorithm. It is shown that, for a quadratic function subject to a linear constraint, all the particular algorithms behave identically if the one-dimensional search for the stepsize is exact. Specifically, they all produce the same sequence of points and lead to the constrained minimal point in no more thann −r descent steps, wherer is the number of linearly independent constraints. The algorithms are then modified so that they can also be employed for a nonquadratic function subject to a nonlinear constraint. Some particular algorithms are tested through several numerical examples.
    Type of Medium: Electronic Resource
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