4.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of TokyoJP154 papers
- National Astronomical Observatory of JapanJP111 papers
- The Graduate University for Advanced Studies, SOKENDAIJP101 papers
- Kavli Institute for the Physics and Mathematics of the UniverseJP67 papers
- Nagoya UniversityJP57 papers
- Hiroshima UniversityJP47 papers
- Centre National de la Recherche ScientifiqueFR45 papers
- Tohoku UniversityJP42 papers
- University College LondonGB42 papers
- Institute of Astronomy and Astrophysics, Academia SinicaTW41 papers
- Korea Astronomy and Space Science InstituteKR41 papers
- Tokyo Institute of TechnologyJP41 papers
18 papers · 1 filter
Concrete convergence rates for common fixed point problems under Karamata regularity
Tianxiang Liu, Bruno F. Lourenço
We introduce the notion of Karamata regular operators, which is a notion of regularity that is suitable for obtaining concrete convergence rates for common fixed point problems. Th…
Majorization-minimization Bregman proximal gradient algorithms for NMF with the Kullback--Leibler divergence
Shota Takahashi, Mirai Tanaka, Shiro Ikeda
Nonnegative matrix factorization (NMF) is a popular method in machine learning and signal processing to decompose a given nonnegative matrix into two nonnegative matrices. In this…
On a minimization problem of the maximum generalized eigenvalue: properties and algorithms
Akatsuki Nishioka, Mitsuru Toyoda, Mirai Tanaka +1
We study properties and algorithms of a minimization problem of the maximum generalized eigenvalue of symmetric-matrix-valued affine functions, which is nonsmooth and quasiconvex,…
Eigenvalue programming beyond matrices
Masaru Ito, Bruno F. Lourenço
In this paper we analyze and solve eigenvalue programs, which consist of the task of minimizing a function subject to constraints on the "eigenvalues" of the decision variable. Her…
Generalized power cones: optimal error bounds and automorphisms
Ying Lin, Scott B. Lindstrom, Bruno F. Lourenço +1
Error bounds are a requisite for trusting or distrusting solutions in an informed way. Until recently, provable error bounds in the absence of constraint qualifications were unatta…
Automorphisms of rank-one generated hyperbolicity cones and their derivative relaxations
Masaru Ito, Bruno F. Lourenço
A hyperbolicity cone is said to be rank-one generated (ROG) if all its extreme rays have rank one, where the rank is computed with respect to the underlying hyperbolic polynomial.…
Self-dual polyhedral cones and their slack matrices
João Gouveia, Bruno F. Lourenço
We analyze self-dual polyhedral cones and prove several properties about their slack matrices. In particular, we show that self-duality is equivalent to the existence of a positive…
Blind Deconvolution with Non-smooth Regularization via Bregman Proximal DCAs
Shota Takahashi, Mirai Tanaka, Shiro Ikeda
Blind deconvolution is a technique to recover an original signal without knowing a convolving filter. It is naturally formulated as a minimization of a quartic objective function u…
New Bregman proximal type algorithms for solving DC optimization problems
Shota Takahashi, Mituhiro Fukuda, Mirai Tanaka
Difference of Convex (DC) optimization problems have objective functions that are differences between two convex functions. Representative ways of solving these problems are the pr…
Hyperbolicity cones are amenable
Bruno F. Lourenço, Vera Roshchina, James Saunderson
Amenability is a notion of facial exposedness for convex cones that is stronger than being facially dual complete (or "nice") which is, in turn, stronger than merely being facially…
Error bounds, facial residual functions and applications to the exponential cone
Scott B. Lindstrom, Bruno F. Lourenço, Ting Kei Pong
We construct a general framework for deriving error bounds for conic feasibility problems. In particular, our approach allows one to work with cones that fail to be amenable or eve…
Convergence analysis under consistent error bounds
Tianxiang Liu, Bruno F. Lourenço
We introduce the notion of consistent error bound functions which provides a unifying framework for error bounds for multiple convex sets. This framework goes beyond the classical…
Random projection of Linear and Semidefinite problem with linear inequalities
Pierre-Louis Poirion, Bruno F. Lourenço, Akiko Takeda
The Johnson-Lindenstrauss Lemma states that there exist linear maps that project a set of points of a vector space into a space of much lower dimension such that the Euclidean dist…
Parallel Randomized Algorithm for Chance Constrained Program
Xun Shen, Jiancang Zhuang, Xingguo Zhang
Chance constrained program is computationally intractable due to the existence of chance constraints, which are randomly disturbed and should be satisfied with a probability. This…
Extension of the LP-Newton method to SOCPs via semi-infinite representation
Takayuki Okuno, Mirai Tanaka
The LP-Newton method solves the linear programming problem (LP) by repeatedly projecting a current point onto a certain relevant polytope. In this paper, we extend the algorithmic…
Generalized subdifferentials of spectral functions over Euclidean Jordan algebras
Bruno F. Lourenço, Akiko Takeda
This paper is devoted to the study of generalized subdifferentials of spectral functions over Euclidean Jordan algebras. Spectral functions appear often in optimization problems pl…
Solving SDP Completely with an Interior Point Oracle
Bruno F. Lourenço, Masakazu Muramatsu, Takashi Tsuchiya
We suppose the existence of an oracle which solves any semidefinite programming (SDP) problem satisfying Slater's condition simultaneously at its primal and dual sides. We note tha…
Numerical studies of the optimization of the first eigenvalue of the heat diffusion in inhomogeneous media
Kaname Matsue, Hisashi Naito
In this paper, we study optimization of the first eigenvalue of the heat equation with spatially nonuniform conductivity on a bounded domain under several constraints for the condu…