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20172022
most citedLinearly Involved Generalized Moreau Enhanced Models and Their Proximal Splitting Algorithm under Overall Convexity Condition

44 citations · 54 across the 6 of their papers we have counts for

collaborators

6 papers

math.OC2022★ 7 cited

Hierarchical Convex Optimization by the Hybrid Steepest Descent Method with Proximal Splitting Operators -- Enhancements of SVM and Lasso

Isao Yamada, Masao Yamagishi

The breakthrough ideas in the modern proximal splitting methodologies allow us to express the set of all minimizers of a superposition of multiple nonsmooth convex functions as the…

math.OC2021

A Convexly Constrained LiGME Model and Its Proximal Splitting Algorithm

Wataru Yata, Masao Yamagishi, Isao Yamada

For the sparsity-rank-aware least squares estimations, the LiGME (Linearly involved Generalized Moreau Enhanced) model was established recently in [Abe, Yamagishi, Yamada, 2020] to…

math.NA2020★ 1 cited

Approximate Simultaneous Diagonalization of Matrices via Structured Low-Rank Approximation

Riku Akema, Masao Yamagishi, Isao Yamada

Approximate Simultaneous Diagonalization (ASD) is a problem to find a common similarity transformation which approximately diagonalizes a given square-matrix tuple. Many data scien…

math.OC2020★ 1 cited

A Hierarchical Convex Optimization for Multiclass SVM Achieving Maximum Pairwise Margins with Least Empirical Hinge-Loss

Yunosuke Nakayama, Masao Yamagishi, Isao Yamada

In this paper, we formulate newly a hierarchical convex optimization for multiclass SVM achieving maximum pairwise margins with least empirical hinge-loss. This optimization proble…

math.OC2019★ 44 cited

Linearly Involved Generalized Moreau Enhanced Models and Their Proximal Splitting Algorithm under Overall Convexity Condition

Jiro Abe, Masao Yamagishi, Isao Yamada

The convex envelopes of the direct discrete measures, for the sparsity of vectors or for the low-rankness of matrices, have been utilized extensively as practical penalties in orde…

math.OC2017★ 1 cited

Quadratic Programming Over Ellipsoids (with Applications to Constrained Linear Regression and Tensor Decomposition)

Anh-Huy Phan, Masao Yamagishi, Danilo Mandic +1

A novel algorithm to solve the quadratic programming problem over ellipsoids is proposed. This is achieved by splitting the problem into two optimisation sub-problems, quadratic pr…