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A Convergent Procedure for Convex Programming
Journal of the Society for Industrial and Applied Mathematics, 1963Gegeben ist eine beliebige Menge \(X\) und ein Vektor \(v(x) = (v_0(x), v_1(x),\dots, v_m(x))\), dessen Komponenten auf \(X\) definierte reellwertige Funktionen sind. Ferner werden definiert: \(u\cdot v\) als Skalarprodukt der Vektoren \(u = (u_0, u_1,\dots, u_m)\) and \(v = (v_0, v_1,\dots, v_m)\), der Wertebereich von \(v(x)\) als \(V = \{v(x)\mid x ...
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Convex infinite horizon programs
Mathematical Programming, 1983We establish conditions under which a sequence of finite horizon convex programs monotonically increases in value to the value of the infinite program; a subsequence of optimal solutions converges to the optimal solution of the infinite problem. If the conditions we impose fail, then (roughtly) the optimal value of the infinite horizon problem is an ...
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Preliminaries: Convex Analysis and Convex Programming
2001In this chapter, we give some definitions and results connected with convex analysis, convex programming, and Lagrangian duality. In Part Two, these concepts and results are utilized in developing suitable optimality conditions and numerical methods for solving some convex problems.
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From Convex to Mixed Programming
1985This paper is concerned with convex programming, differential programming and mixed programming. All spaces involved will assumed to be linear, normed and complete, so that we limit ourselves to Banach spaces, although in a number of cases locally convex topological vector spaces would do as well.
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A Maximum-Likelihood TDOA Localization Algorithm Using Difference-of-Convex Programming
IEEE Signal Processing Letters, 2021Tareq Y Al-Naffouri +2 more
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PENNON: A code for convex nonlinear and semidefinite programming
Optimization Methods and Software, 2003Michael Stingl, Michal Kocvara
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Entropy-Like Proximal Methods in Convex Programming
Mathematics of Operations Research, 1994Marc Teboulle +2 more
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