4 papers
Theoretical Validation of the Latent Optimally Partitioned- Penalty with Application to Angular Power Spectrum Estimation
Hiroki Kuroda, Renato Luis Garrido Cavalcante, Masahiro Yukawa
This paper demonstrates that, in both theory and practice, the latent optimally partitioned (LOP)- penalty is effective for exploiting block-sparsity without knowled…
Nonconvex Latent Optimally Partitioned Block-Sparse Recovery via Log-Sum and Minimax Concave Penalties
Takanobu Furuhashi, Hiroki Kuroda, Masahiro Yukawa +3
We propose two nonconvex regularization methods, LogLOP-l2/l1 and AdaLOP-l2/l1, for recovering block-sparse signals with unknown block partitions. These methods address the underes…
Sum-Rate Maximization via Convex Optimization Using Subgradient Projections Onto Nonlinear Spectral Radius Constraint Sets
Hiroki Kuroda, Renato Luis Garrido Cavalcante
We solve the (weighted) sum-rate maximization problem over the set of achievable rates characterized by a nonlinear spectral radius function. This set has been recently shown to be…
A Convex-Nonconvex Framework for Enhancing Minimization Induced Penalties
Hiroki Kuroda
This paper presents a novel framework for nonconvex enhancement of minimization induced (MI) penalties while preserving the overall convexity of associated regularization models. M…