Silvia Gandy
Tokyo Institute of Technology
5 Papers
36 Citations
Silvia Gandy is an academic researcher from Tokyo Institute of Technology. The author has contributed to research in topics: Convex optimization & Greedy algorithm. The author has an hindex of 4, co-authored 5 publications.
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Papers
Tensor completion and low-n-rank tensor recovery via convex optimization
TL;DR: This paper uses the n-rank of a tensor as a sparsity measure and considers the low-n-rank tensor recovery problem, i.e. the problem of finding the tensor of the lowest n-Rank that fulfills some linear constraints.
Convex optimization techniques for the efficient recovery of a sparsely corrupted low-rank matrix
Silvia Gandy,Isao Yamada +1 more
- 04 Oct 2010
TL;DR: This work proposes an algorithm based on the Douglas-Rachford splitting technique which has inherent convergence guarantees and proposes, based on algorithms from rank minimization and sparse vector recovery, a computation- ally efficient greedy algorithm that scales better to large problem sizes than existing algorithms.
20
Sparsity-aware adaptive filtering based on a Douglas-Rachford splitting
Isao Yamada,Silvia Gandy,Masao Yamagishi +2 more
- 29 Aug 2011
TL;DR: This paper proposes a novel online scheme based on a formulation of the adaptive filtering problem as a minimization of the sum of (possibly nonsmooth) convex functions that achieves a monotone decrease of an upper bound of the distance to the solution set of the minimization under certain conditions.
13
Alternating minimization techniques for the efficient recovery of a sparsely corrupted low-rank matrix
Silvia Gandy,Isao Yamada +1 more
- 14 Mar 2010
TL;DR: This work addresses the problem of recovering a low-rank matrix that has a small fraction of its entries arbitrarily corrupted and proposes a computationally efficient greedy algorithm that scales better to large problem sizes than existing algorithms.
5
Optimizing a Particular Real Root of a Polynomial by a Special Cylindrical Algebraic Decomposition
TL;DR: An efficient symbolic method is proposed for solving the optimization problem based on a special cylindrical algebraic decomposition algorithm, which asks for a semi-algebraic decom composition into cells in terms of number-of-roots-invariance.