activity
20182023
most citedMajorization-Minimization-Based Levenberg--Marquardt Method for Constrained Nonlinear Least Squares

22 citations · 40 across the 7 of their papers we have counts for

collaborators

7 papers

cs.GT2023★ 2 cited

Analytically Tractable Models for Decision Making under Present Bias

Yasunori Akagi, Naoki Marumo, Takeshi Kurashima

Time-inconsistency is a characteristic of human behavior in which people plan for long-term benefits but take actions that differ from the plan due to conflicts with short-term ben…

math.OC2022★ 1 cited

Accelerated-gradient-based generalized Levenberg--Marquardt method with oracle complexity bound and local quadratic convergence

Naoki Marumo, Takayuki Okuno, Akiko Takeda

Minimizing the sum of a convex function and a composite function appears in various fields. The generalized Levenberg--Marquardt (LM) method, also known as the prox-linear method,…

cs.SI2021★ 4 cited

A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion

Naoki Marumo, Atsushi Miyauchi, Akiko Takeda +1

Many social phenomena are triggered by public opinion that is formed in the process of opinion exchange among individuals. To date, from the engineering point of view, a large body…

stat.ML2021★ 1 cited

Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm

Yasunori Akagi, Naoki Marumo, Hideaki Kim +2

The importance of aggregated count data, which is calculated from the data of multiple individuals, continues to increase. Collective Graphical Model (CGM) is a probabilistic appro…

math.OC2020★ 22 cited

Majorization-Minimization-Based Levenberg--Marquardt Method for Constrained Nonlinear Least Squares

Naoki Marumo, Takayuki Okuno, Akiko Takeda

A new Levenberg--Marquardt (LM) method for solving nonlinear least squares problems with convex constraints is described. Various versions of the LM method have been proposed, thei…

cs.DS2019

Best-first Search Algorithm for Non-convex Sparse Minimization

Shinsaku Sakaue, Naoki Marumo

Non-convex sparse minimization (NSM), or -constrained minimization of convex loss functions, is an important optimization problem that has many machine learning application…

quant-ph2018★ 10 cited

Perfect discrimination of non-orthogonal quantum states with posterior classical partial information

Seiseki Akibue, Go Kato, Naoki Marumo

The indistinguishability of non-orthogonal pure states lies at the heart of quantum information processing. Although the indistinguishability reflects the impossibility of measurin…