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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 76 (1993), S. 183-193 
    ISSN: 1573-2878
    Keywords: Minimum norm solutions ; duality ; relations between primal and dual solutions
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This note investigates the problem $$\min x_p^p /p,s.t.Ax \geqslant b,$$ where 1〈p〈∞. It is proved that the dual of this problem has the form $$\max b^T y - A^T y_q^q /q,s.t.y \geqslant 0,$$ whereq=p/(p−1). The main contribution is an explicit rule for retrieving a primal solution from a dual one. If an inequality is replaced by an equality, then the corresponding dual variable is not restricted to stay nonnegative. A similar modification exists for interval constraints. Partially regularized problems are also discussed. Finally, we extend an observation of Luenberger, showing that the dual of $$\min x_p ,s.t.Ax \geqslant b,$$ is $$\max b^T y,s.t.y \geqslant 0,A^T y_q \leqslant 1,$$ and sharpening the relation between a primal solution and a dual solution.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 94 (1997), S. 561-590 
    ISSN: 1573-2878
    Keywords: Theorems of the alternative ; duality ; minimum norm duality theorem ; steepest descent directions ; least norm problems ; alignment ; constructive optimality conditions ; degeneracy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper investigates the relations between theorems of the alternative and the minimum norm duality theorem. A typical theorem of the alternative is associated with two systems of linear inequalities and/or equalities, a primal system and a dual one, asserting that either the primal system has a solution, or the dual system has a solution, but never both. On the other hand, the minimum norm duality theorem says that the minimum distance from a given point z to a convex set $$\mathbb{K}$$ is equal to the maximum of the distances from z to the hyperplanes separating z and $$\mathbb{K}$$ . We consider the theorems of Farkas, Gale, Gordan, and Motzkin, as well as new theorems that characterize the optimality conditions of discrete l 1-approximation problems and multifacility location problems. It is shown that, with proper choices of $$\mathbb{K}$$ , each of these theorems can be recast as a pair of dual problems: a primal steepest descent problem that resembles the original primal system, and a dual least–norm problem that resembles the original dual system. The norm that defines the least-norm problem is the dual norm with respect to that which defines the steepest descent problem. Moreover, let y solve the least norm problem and let r denote the corresponding residual vector. If r=0, which means that z ∈ $$\mathbb{K}$$ , then y solves the dual system. Otherwise, when r≠0 and z ∉ $$\mathbb{K}$$ , any dual vector of r solves both the steepest descent problem and the primal system. In other words, let x solve the steepest descent problem; then, r and x are aligned. These results hold for any norm on $$\mathbb{R}^n $$ . If the norm is smooth and strictly convex, then there are explicit rules for retrieving x from r and vice versa.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 64 (1990), S. 429-432 
    ISSN: 1573-2878
    Keywords: Least-distance problems ; least-square problems with nonnegative variables ; active set methods ; row relaxation methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This note suggests new ways for calculating the point of smallest Euclidean norm in the convex hull of a given set of points inR n . It is shown that the problem can be formulated as a linear least-square problem with nonnegative variables or as a least-distance problem. Numerical experiments illustrate that the least-square problem is solved efficiently by the active set method. The advantage of the new approach lies in the solution of large sparse problems. In this case, the new formulation permits the use of row relaxation methods. In particular, the least-distance problem can be solved by Hildreth's method.
    Type of Medium: Electronic Resource
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