On the Approximability of Injective Tensor Norm Vijay Bhattiprolu
On the Approximability of Injective Tensor Norm Vijay Bhattiprolu CMU-CS-19-115 June 28, 2019 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Venkatesan Guruswami, Chair Anupam Gupta David Woodruff Madhur Tulsiani, TTIC Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Copyright c 2019 Vijay Bhattiprolu This research was sponsored by the National Science Foundation under award CCF-1526092, and by the National Science Foundation under award 1422045. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Keywords: Injective Tensor Norm, Operator Norm, Hypercontractive Norms, Sum of Squares Hierarchy, Convex Programming, Continuous Optimization, Optimization over the Sphere, Approximation Algorithms, Hardness of Approximation Abstract The theory of approximation algorithms has had great success with combinatorial opti- mization, where it is known that for a variety of problems, algorithms based on semidef- inite programming are optimal under the unique games conjecture. In contrast, the ap- proximability of most continuous optimization problems remains unresolved. In this thesis we aim to extend the theory of approximation algorithms to a wide class of continuous optimization problems captured by the injective tensor norm framework. Given an order-d tensor T, and symmetric convex sets C1,... Cd, the injective tensor norm of T is defined as sup hT , x1 ⊗ · · · ⊗ xdi, i x 2Ci Injective tensor norm has manifestations across several branches of computer science, optimization and analysis.
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