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Titus Pința

Postdoc at ENSTA Paris

numerical optimization and non-smooth analysis, from a geometric point of view

Personal Data

Titus Octavian Pința

titus.pinta@ensta-paris.fr

www.github.com/titus-pinta

www.gitlab.com/titus.pinta

0000-0002-7027-4774

tituspinta.xyz

Date of Birth March 22nd 1998 | Nationality Romanian

Work Experience

Oct. 2024 – present
  • Postdoc
  • École Nationale Supérieure de Techniques Avancées, Paris
2018 – 2019
  • Internship
  • Tiberiu Popoviciu Institute of Numerical Analysis, Romanian Academy, Cluj-Napoca
  • Seminar Talks in Numerical Linear Algebra and Approximation
  • ictp.acad.ro/titus-pinta

Education

2020 – 2024
  • PhD in Mathematics—Summa Cum Laude
  • Georg-August-Universität Göttingen, Germany
  • thesis title: Newton-type Methods
  • advisor: D. Russell Luke
  • defense date: 23 September 2024
2019 – 2020
  • MSc Mathematical Modelling and Scientific Computing
  • Mathematical Institute, Oxford University, United Kingdom
  • thesis title: Random Subspace Methods for Nonlinear Optimization
  • with distinction
2016 – 2020
  • BSc Mathematics and Computer Science
  • Universitatea Babeș Bolyai, Cluj-Napoca, Romania
  • thesis title: Gradient Descent Optimization for Neural Network Training
  • grade: 9.86/10
2012 – 2016
  • High School
  • Colegiul Național Emanuil Gojdu, Oradea, Romania
  • national olympiad in computer science, 2015
  • national olympiad in mathematics, 2014
  • baccalauréat mathematics: 10
  • baccalauréat computer science: 9.71

Teaching Experience

Winter 2025Differentiable Optimization I
Winter 2024Numerical Analysis
Summer 2023Mathematics for Computer Science Students
Winter 2022Operations Research
Summer 2021Optimization
Winter 2020Numerical Variational Analysis
Winter 2020Graph Theory

Reviewer for

Projects

  1. FMJH thematic postdoc—Inertial Acceleration in Higher Order Methods (ANR-22-EXES-0013)
  2. Hi! PARIS—Higher Order Methods for Machine Learning (offer declined)

Publications

  1. T. Pința, A Newton-type Method for Non-smooth Under-determined Systems of Equations. Numer. Algorithms. (under review).
  2. T. Pința, A Stochastic Newton-type Method for Non-smooth Optimization. Math. Program. (under review).
  3. E. Cohen, D. R. Luke, T. Pința, S. Sabach, and M. Teboulle, A semi-Bregman proximal alternating method for a class of nonconvex problems: local and global convergence analysis. J. Global Optim. 89 (2024).
  4. S. C. László, C. Alecsa, and T. Pința, An Extension of the Second Order Dynamical System that Models Nesterov's Convex Gradient Method. Appl. Math. Optim. 84 (2021).
  5. A. Viorel, C. Alecsa, and T. Pința, Asymptotic analysis of a structure-preserving integrator for damped Hamiltonian systems. Discrete Contin. Dyn. Syst. 41 (2021).
  6. C. Alecsa, T. Pința, and I. Boroș, New optimization algorithms for neural network training using operator splitting techniques. Neural Networks. 126 (2020).

Invited Talks

  1. Newton differentiability and non-smooth Newton type methods, Séminaire Parisien d'Optimisation, Paris 2025
  2. A Newton-type Method for Constrained Optimization, PGMO Days, Paris 2024
  3. Operator Splitting Based Newton-type Method for Constrained Optimization, Optimization Techniques in Quantum Chemistry, Aachen 2024

Conference Talks

  1. A Newton-type Method for Multicriteria Optimization, Spring School and Workshop on Variational Analysis and Optimization, Hanoi 2025
  2. A Newton-type Method for Constrained Optimization, 4th International Conference on Variational Analysis and Optimization, Santiago de Chile 2025
  3. Newton-type Methods in Abstract Metric Spaces (poster), Congrès des Jeunes Chercheur.e.s en Mathématiques Appliquées, Lyon 2024
  4. Random Newton-type Iterations with Application to Electronic Structure Determination, 21st EUROpt Workshop, Lund 2024
  5. Newton-type Methods in Abstract Metric Spaces (poster), Workshop on Nonsmooth Optimization and Applications, Antwerp 2024
  6. Operator Splitting Based Newton-type Method for Constrained Optimization, 20th EUROpt Workshop: Continuous Optimization Working Group is coming home, Budapest 2023
  7. Operator Splitting Based Newton-type Method for Constrained Optimization, SIAM Conference on Optimization, Seattle 2023
  8. Non-Euclidean Proximal Algorithms for Quadratic Composite Optimization: The Case of Mean Curvature Flow, GAMM Annual Meeting, Aachen 2022
  9. Non-Euclidean Proximal Algorithms for Quadratic-Composite Optimization, SIAM Conference on Imaging Science, online 2022
  10. Gradient Descent Methods for Machine Learning, Zilele Academice Clujene, Tiberiu Popoviciu Institute of Numerical Analysis, Cluj-Napoca 2019

Workshops

  1. 8th RTG 2088 Workshop: Discovering structure in complex data: Statistics meets Optimization and Inverse Problems, Mariaspring 2023
  2. 7th RTG 2088 Workshop: Discovering structure in complex data: Statistics meets Optimization and Inverse Problems, Goslar 2022
  3. 6th RTG 2088 Workshop: Discovering structure in complex data: Statistics meets Optimization and Inverse Problems, Mariaspring 2021
  4. 23rd Internet Seminar Evolutionary Equations, online 2020
  5. 22nd Internet Seminar Ergodic Theorems, Systems with Quasi-Discrete Spectrum, Wuppertal 2019
  6. Numerical Linear Algebra at Tiberiu Popoviciu Institute of Numerical Analysis, Cluj-Napoca 2018
  7. Approximation Theory at Tiberiu Popoviciu Institute of Numerical Analysis, Cluj-Napoca 2018

News

[May. 2025] I will be organizing a Mini-Symposium at the SMAI conference

[Apr. 2025] My paper A Newton-type Method for Non-smooth Under-determined Systems of Equations is on arxiv

[Feb. 2025] My paper A Stochastic Newton-type Method for Non-smooth Optimization is on arxiv