3,438 research outputs found
A Slightly Lifted Convex Relaxation for Nonconvex Quadratic Programming with Ball Constraints
Globally optimizing a nonconvex quadratic over the intersection of balls
in is known to be polynomial-time solvable for fixed .
Moreover, when , the standard semidefinite relaxation is exact. When
, it has been shown recently that an exact relaxation can be constructed
using a disjunctive semidefinite formulation based essentially on two copies of
the case. However, there is no known explicit, tractable, exact convex
representation for . In this paper, we construct a new, polynomially
sized semidefinite relaxation for all , which does not employ a disjunctive
approach. We show that our relaxation is exact for . Then, for ,
we demonstrate empirically that it is fast and strong compared to existing
relaxations. The key idea of the relaxation is a simple lifting of the original
problem into dimension . Extending this construction: (i) we show that
nonconvex quadratic programming over has an
exact semidefinite representation; and (ii) we construct a new relaxation for
quadratic programming over the intersection of two ellipsoids, which globally
solves all instances of a benchmark collection from the literature
A Deterministic Theory for Exact Non-Convex Phase Retrieval
In this paper, we analyze the non-convex framework of Wirtinger Flow (WF) for
phase retrieval and identify a novel sufficient condition for universal exact
recovery through the lens of low rank matrix recovery theory. Via a perspective
in the lifted domain, we show that the convergence of the WF iterates to a true
solution is attained geometrically under a single condition on the lifted
forward model. As a result, a deterministic relationship between the accuracy
of spectral initialization and the validity of {the regularity condition} is
derived. In particular, we determine that a certain concentration property on
the spectral matrix must hold uniformly with a sufficiently tight constant.
This culminates into a sufficient condition that is equivalent to a restricted
isometry-type property over rank-1, positive semi-definite matrices, and
amounts to a less stringent requirement on the lifted forward model than those
of prominent low-rank-matrix-recovery methods in the literature. We
characterize the performance limits of our framework in terms of the tightness
of the concentration property via novel bounds on the convergence rate and on
the signal-to-noise ratio such that the theoretical guarantees are valid using
the spectral initialization at the proper sample complexity.Comment: In Revision for IEEE Transactions on Signal Processin
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