Preprints

• João Gouveia, Masaru Ito, and Bruno F. Lourenço, "Minimal hyperbolic polynomials and ranks of homogeneous cones," April 2024.

Journal Articles

• Masaru Ito and Bruno F. Lourenço, "Eigenvalue programming beyond matrices," Computational Optimization and Applications, Online first, July 2024.
• Masaru Ito, Zhaosong Lu and Chuan He, "A parameter-free conditional gradient method for composite minimization under Hölder condition," Journal of Machine Learning Research, 24(166), pp.1--34, 2023.
• Masaru Ito and Bruno F. Lourenço, "Automorphisms of rank-one generated hyperbolicity cones and their derivative relaxations," SIAM Journal on Applied Algebra and Geometry, 7, pp.236--263, 2023.
• Masaru Ito and Mituhiro Fukuda, "Nearly optimal first-order methods for convex optimization under gradient norm measure: An adaptive regularization approach," Journal of Optimization Theory and Applications, 188, pp.770--804, 2021.
• Koichiro Akiyama, Shuhei Nakamura, Masaru Ito, and Noriko Hirata-Kohno, "A key exchange protocol relying on polynomial maps," International Journal of Mathematics for Industry, 11(1), 2019. DOI:10.1142/S2661335219500035
• Masaru Ito and Bruno F. Lourenço, "The automorphism group and the non-self-duality of p-cones," Journal of Mathematical Analysis and Applications, 471, pp.392--410, 2019.
• Masaru Ito and Bruno F. Lourenço, "The p-cones in dimension $n \geq 3$ are not homogeneous when $p \neq 2$," Linear Algebra and its Applications, 533, pp.326--335, 2017.
• Masaru Ito and Bruno F. Lourenço, "A bound on the Carathéodory number," Linear Algebra and its Applications, 532(1), pp.347--363, 2017.
• Masaru Ito and Mituhiro Fukuda, "A family of subgradient-based methods for convex optimization problems in a unifying framework," Optimization Methods and Software, 31(5), pp.952--982, 2016.
• Masaru Ito, "New results on subgradient methods for strongly convex optimization problems with a unified analysis," Computational Optimization and Applications, 65(1), pp.127--172, 2016.
• Masaru Ito and Noriko Hirata-Kohno, "Optimization for Lattices and Diophantine Approximations," Interdisciplinary Information Sciences 19(2), pp.135--142, 2013.

• Masaru Ito, "Adaptive gradient-based method for convex optimization problems under error bounds," 10th International Congress on Industrial and Applied Mathematics (ICIAM2023), Session 00815, Tokyo, Japan, August 2023.
• Masaru Ito and Bruno F. Lourenço, "Optimization problems with eigenvalue constraints," SIAM Conference on Optimization (OP23), MS147, Seattle, Washington, USA, May 2023. [slide]
• Masaru Ito and Bruno F. Lourenço, "Automorphism Groups of the Derivative Relaxations of Rank One Generated Hyperbolicity Cones," The seventh International Conference on Continuous Optimization (ICCOPT/MOPTA 2022), Bethlehem, Pennsylvania, USA, July 2022. [slide]
• Masaru Ito, Zhaosong Lu and Chuan He, "Adaptive Step-Size Rule for Conditional Gradient Methods Minimizing Weakly Smooth Objective Functions," SIAM Conference on Optimization (OP21), MS69, July 2021 (virtual conference). [slide]
• Masaru Ito and Mituhiro Fukuda, "Nearly optimal first-order method under Hölderian error bound: An adaptive proximal point approach," The Sixth International Conference on Continuous Optimization (ICCOPT2019), Berlin, German, August 2019. [slide]
• Masaru Ito and Mituhiro Fukuda, "An adaptive first order method for weakly smooth and uniformly convex problems," The 23rd International Symposium on Mathematical Programming (ISMP2018), Bordeaux, France, July 2018.
• Masaru Ito and Bruno F. Lourenço, "A Bound on the Carathéodory Number," SIAM Conference on Optimization (OP17), CP15, Vancouver, Canada, May 2017.
• Masaru Ito, "An Adaptive Restarting for Universal Gradient Method of Minimizing Strongly Convex Functions," The Fifth International Conference on Continuous Optimization, Tue.C.5K, Tokyo, Japan, August 2016.
• Masaru Ito, "New Results on Subgradient Methods for Weakly Smooth and Strongly Convex Problems," the 22nd International Symposium on Mathematical Programming (ISMP 2015) , TD22, Pittuburgh, USA, July, 2015.
• Masaru Ito and Mituhiro Fukuda, "A unified framework for subgradient algorithms minimizing strongly convex functions," SIAM Conference on Optimization, CP22, San Diego, California, USA, May 2014.
• Masaru Ito, "A unified framework of subgradient algorithms for convex optimization problems," The Fourth International Conference on Continuous Optimization, Mon.C.22, Lisbon, Portugal, July 2013.
• Masaru Ito, "Optimization for lattices and Diophantine approximation," Diophantine Analysis and Related Fields 2013, Tokyo, Japan, January 2013.