1 citations · 1 across the 9 of their papers we have counts for
9 papers
Parameter-Free Cubic-Regularized Newton Method: Sharp Complexity and Generalized Smoothness
Shaoying Fang, Naoki Marumo, Akiko Takeda
We analyze a variant of the cubic-regularized Newton method for nonconvex optimization. This variant is parameter-free in that it requires no prior knowledge of problem-dependent p…
A General Recipe for Parameter-Free Nonconvex Optimization via Higher-Order Regularization
Naoki Marumo, Akiko Takeda
We develop a systematic framework for constructing parameter-free algorithms for smooth nonconvex optimization. The framework is based on higher-order regularization: each step is…
Practical Regularized Quasi-Newton Methods with Inexact Function Values
Hiroki Hamaguchi, Naoki Marumo, Akiko Takeda
Many practical optimization problems involve objective function values that are corrupted by unavoidable numerical errors. In smooth nonconvex optimization, quasi-Newton methods co…
Complexity and convergence analysis of a single-loop SDCAM for Lipschitz composite optimization and beyond
Hao Zhang, Naoki Marumo, Ting Kei Pong +1
We develop and analyze a single-loop algorithm for minimizing the sum of a Lipschitz differentiable function , a prox-friendly proper closed function (with a closed domain o…
A Regression-Based Prediction-Correction Method for Stochastic Time-Varying Optimization Problems
Tomoya Kamijima, Naoki Marumo, Akiko Takeda
In many real-world applications, optimization problems evolve continuously over time and are often subject to stochastic noise. We consider a stochastic time-varying optimization p…
A Simple yet Highly Accurate Prediction-Correction Algorithm for Time-Varying Optimization
Tomoya Kamijima, Naoki Marumo, Akiko Takeda
This paper proposes a simple yet highly accurate prediction-correction algorithm, SHARP, for unconstrained time-varying optimization problems. Its prediction is based on an extrapo…
Initial Placement for Fruchterman--Reingold Force Model With Coordinate Newton Direction
Hiroki Hamaguchi, Naoki Marumo, Akiko Takeda
The Fruchterman--Reingold (FR) force model is widely used in force-directed graph drawing, and multilevel approaches such as sfdp in Graphviz scale these methods effectively. A cru…
Heavy-ball Differential Equation Achieves Convergence for Nonconvex Functions
Kaito Okamura, Naoki Marumo, Akiko Takeda
First-order optimization methods for nonconvex functions with Lipschitz continuous gradient and Hessian have been extensively studied. State-of-the-art methods for finding an $\var…
Prediction-Correction Algorithm for Time-Varying Smooth Non-Convex Optimization
Hidenori Iwakiri, Tomoya Kamijima, Shinji Ito +1
Time-varying optimization problems are prevalent in various engineering fields, and the ability to solve them accurately in real-time is becoming increasingly important. The predic…