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Uniqueness and Pseudo-Convexity

Claude Zuily

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Abstract

In the results described before, no condition (except to be non-characteristic) was imposed on the initial hypersurface and in particular, uniqueness did not depend on the side containing the support of the Solution; however, precise hypotheses like smoothness, muitiplicity of the characteristic roots, were made. In the general case (where no smoothness occurs) we shall see that uniqueness depends on geometrical conditions between the operator and the hypersurface, called “pseudo-convexity conditions”. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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What this paper is about

In the results described before, no condition (except to be non-characteristic) was imposed on the initial hypersurface and in particular, uniqueness did not depend on the side containing the support of the Solution; however, precise hypotheses like smoothness, muitiplicity of the characteristic roots, were made. In the general case (where no smoothness occurs) we shall see that uniqueness depends on geometrical conditions between the operator and the hypersurface, called “pseudo-convexity conditions”. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Available abstract

In the results described before, no condition (except to be non-characteristic) was imposed on the initial hypersurface and in particular, uniqueness did not depend on the side containing the support of the Solution; however, precise hypotheses like smoothness, muitiplicity of the characteristic roots, were made. In the general case (where no smoothness occurs) we shall see that uniqueness depends on geometrical conditions between the operator and the hypersurface, called “pseudo-convexity conditions”. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Key concepts: Hypersurface, Uniqueness, Convexity, Smoothness, Mathematics, Operator (biology), Mathematical analysis, Pure mathematics

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