【摘 要】
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In recent years,norm-constrained polynomial optimization has found applications in many different areas,including spectral theory of tensors,signal processi
【机 构】
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TheChineseUniversityofHongKong
【出 处】
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International Conference on the spectral theory of the tenso
论文部分内容阅读
In recent years,norm-constrained polynomial optimization has found applications in many different areas,including spectral theory of tensors,signal processing,data analysis and quantum physics.Given their generality,norm-constrained polynomial optimization problems are typically intractable,which leads to the question of their approximability.In this talk,we will discuss the close connection between norm-constrained polynomial optimization and the algorithmic theory of convex bodies.Then,we will demonstrate how techniques from the latter can be used to prove the best-known-to-date approximation results for various classes of norm-constrained polynomial optimization problems.
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