arXiv subjects

Yoichi Izunaga

Publications and source records attributed to Yoichi Izunaga.

3 recordsLinked to original sources

arXiv2026

An Axiomatic Analysis of Distributionally Robust Optimization with $q$-Norm Ambiguity Sets for Probability Smoothing

We analyze the axiomatic properties of a class of probability estimators derived from Distributionally Robust Optimization (DRO) with $q$-norm ambiguity sets ($q$-DRO), a principled approach to the zero-frequency problem. While classical estimators such as Laplace smoothing are characterized by strong linearity axioms like Ratio Preservation, we show that $q$-DRO provides a flexible alternative that satisfies other desirable properties. We first prove that for any $q \in [1, \infty]$, the $q$-DRO estimator satisfies the fundamental axioms of Positivity and Symmetry. For the case of $q \in (1, \infty)$, we then prove that it also satisfies Order Preservation. Our analysis of the optimality conditions also reveals that the $q$-DRO formulation is equivalent to the regularized empirical loss minimization.

math.OC↗

arXiv2025

A scalable mixed-integer conic optimization approach to cardinality-constrained Poisson regression with safe screening

This paper introduces a novel approach for cardinality-constrained Poisson regression to address feature selection challenges in high-dimensional count data. We formulate the problem as a mixed-integer conic optimization, enabling the use of modern solvers for optimal solutions. To enhance computational efficiency, we develop a safe screening based on Fenchel conjugates, thereby effectively removing irrelevant features before optimization. Experiments on synthetic datasets demonstrate that our safe screening significantly reduces the problem size, leading to substantial improvements in computational time. Our approach can solve Poisson regression problems with tens of thousands of features, exceeding the scale of previous studies. This work provides a valuable tool for interpretable feature selection in high-dimensional Poisson regression.

math.OC↗

arXiv2017

A branch-and-price approach with MILP formulation to modularity density maximization on graphs

For clustering of an undirected graph, this paper presents an exact algorithm for the maximization of modularity density, a more complicated criterion to overcome drawbacks of the well-known modularity. The problem can be interpreted as the set-partitioning problem, which reminds us of its integer linear programming (ILP) formulation. We provide a branch-and-price framework for solving this ILP, or column generation combined with branch-and-bound. Above all, we formulate the column generation subproblem to be solved repeatedly as a simpler mixed integer linear programming (MILP) problem. Acceleration techniques called the set-packing relaxation and the multiple-cutting-planes-at-a-time combined with the MILP formulation enable us to optimize the modularity density for famous test instances including ones with over 100 vertices in around four minutes by a PC. Our solution method is deterministic and the computation time is not affected by any stochastic behavior. For one of them, column generation at the root node of the branch-and-bound tree provides a fractional upper bound solution and our algorithm finds an integral optimal solution after branching.

cs.SI↗