SOURCE-LINKED INTELLIGENCE

Symmetry and Optimization at the Frontiers of Computation

CORDIS · observation · Publication date unknown

izes convex optimization from Euclidean space to the far more general setting of curved spaces with symmetries. Its unfamiliar kind of convexity has recently received much attention in statistics and machine learning. The symmetries are realized by noncommutative groups and imply a high degree of algebraic structure. This combination of symmetry and optimization promises to be key to fast algorithms and deep structural insight. Noncommutative group optimization connects important problems across a wide range of disciplines that appear unrelated at first glance: program testing and derandomization in computer science, estimation problems in statistics, isomorphism problems in algebra, the P vs NP problem and circuit lower bounds in complexity theory, optimal transport in machine learning, marginal and entanglement problems in quantum information, and optimization on quantum computers. This list contains both discrete and continuous problems, theoretical and applied ones, for classical as well as for quantum computers. They have been studied separately over many years by many authors.

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR