Results 271 to 280 of about 12,325 (304)
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Variational Inequality with Fuzzy Convex Cone
Journal of Global Optimization, 1999In this interesting paper, the authors have studied the variational inequality with fuzzy cones and several properties of the fuzzy cones. Using the technique of the multiple objective programming, a numerical method is developed for solving the variational inequality problem with fuzzy convex cones.
Hsiao-Fan Wang, Hsueh-Ling Liao
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Normality and Nuclearity of Convex Cones
Positivity, 2007The author gives characterizations of normality and nuclearity of convex cones in topological vector spaces. A sufficient condition for a weakly normal cone to be normal (respectively, nuclear) is also presented.
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Separation of Convex Cones and Extremal Problems
Optimization Methods and Software, 2005In 1958 the author proved the Maximum Principle [2]. B. Pshenichni wrote that the proof was sensational, using topology to obtain a result of variational calculus. Later the author worked out the Tent Method [3] as a general way to solve extremal problems. In fact, main ideas of the Method were contained in [2].
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Journal of Optimization Theory and Applications, 1988
We point out in this note that the class of cones in a locally convex topological vector space satisfying property (\(\Pi)\) or piecewise relatively weakly compact cones is exactly the class of cones admitting weakly compact bases or the class of cones whose closures admit weakly compact bases.
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We point out in this note that the class of cones in a locally convex topological vector space satisfying property (\(\Pi)\) or piecewise relatively weakly compact cones is exactly the class of cones admitting weakly compact bases or the class of cones whose closures admit weakly compact bases.
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1993
A cone C in R n is a set of points such that if x∈ C, then so is every nonnegative scalar multiple of x, i.e., if x∈C, then λx∈C for 0 ≤ λ∈R, x∈R n (see Figure 4.1.a for C in R2). If we consider the set of points X = {x}, then the cone generated by X is C = {y|y = λx, 0 ≤ λ∈ R, x∈X}. And if 0∉ X and for each y(≠0)∈C there are unique x∈X with λ > 0 such
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A cone C in R n is a set of points such that if x∈ C, then so is every nonnegative scalar multiple of x, i.e., if x∈C, then λx∈C for 0 ≤ λ∈R, x∈R n (see Figure 4.1.a for C in R2). If we consider the set of points X = {x}, then the cone generated by X is C = {y|y = λx, 0 ≤ λ∈ R, x∈X}. And if 0∉ X and for each y(≠0)∈C there are unique x∈X with λ > 0 such
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Lifts of Non-Compact Convex Sets and Cone Factorizations
Journal of Systems Science and Complexity, 2020Lihong Zhi, Zhi Lihong
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Radial symmetry and partially overdetermined problems in a convex cone
Mathematische Nachrichten, 2023Keomkyo Seo
exaly