World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
43
Citations
9457
World Ranking
1674
National Ranking
97

Engineering and Technology

D-Index
41
Citations
8799
World Ranking
6845
National Ranking
227

Overview

Massimo Fornasier is affiliated with the Technical University of Munich in Germany. Their research spans multiple fields including computer science, mathematics, and engineering, with a strong focus on applied and computational aspects.

The researcher has contributed extensively to various subfields such as:

  • Artificial Intelligence
  • Computational Mechanics
  • Applied Mathematics
  • Statistical and Nonlinear Physics
  • Computer Networks and Communications

Fornasier's work addresses diverse topics, among them:

  • Sparse and Compressive Sensing Techniques
  • Model Reduction and Neural Networks
  • Distributed Control Multi-Agent Systems
  • Stochastic Gradient Optimization Techniques
  • Mathematical Biology Tumor Growth
  • Neural Networks and Applications
  • Evolutionary Game Theory and Cooperation

Their recent notable papers include:

  • "A Relaxed Kačanov iteration for the p-poisson problem" (2020, Numerische Mathematik)
  • "Anisotropic Diffusion in Consensus-Based Optimization on the Sphere" (2022, SIAM Journal on Optimization)
  • "Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning" (2020, arXiv (Cornell University))
  • "A measure theoretical approach to the mean-field maximum principle for training NeurODEs" (2022, Nonlinear Analysis)
  • "Consensus-Based Optimization Methods Converge Globally" (2024, SIAM Journal on Optimization)

Fornasier has collaborated frequently with several researchers, including:

  • Timo Klock
  • Konstantin Riedl
  • Hui Huang
  • Michael Rauchensteiner
  • Lukang Sun

Their publications have appeared in a range of venues such as:

  • arXiv (Cornell University)
  • European Journal of Applied Mathematics
  • SIAM Journal on Optimization
  • Applied and Computational Harmonic Analysis
  • Numerische Mathematik

Best Publications

  • Iteratively reweighted least squares minimization for sparse recovery

    Ingrid Daubechies;Ronald DeVore;Massimo Fornasier;C. Si̇nan Güntürk

  • Iteratively re-weighted least squares minimization for sparse recovery

    Ingrid Daubechies;Ronald DeVore;Massimo Fornasier;C. Sinan Gunturk

  • Asymptotic Flocking Dynamics for the Kinetic Cucker–Smale Model

    José A. Carrillo;M. Fornasier;Jesús Rosado;Giuseppe Toscani

  • Particle, kinetic, and hydrodynamic models of swarming

    José A. Carrillo;Massimo Fornasier;Giuseppe Toscani;Francesco Vecil

  • Accelerated Projected Gradient Method for Linear Inverse Problems with Sparsity Constraints

    Ingrid Daubechies;Massimo Fornasier;Ignace Loris

  • Recovery Algorithms for Vector-Valued Data with Joint Sparsity Constraints

    Massimo Fornasier;Holger Rauhut

  • Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization

    Massimo Fornasier;Holger Rauhut;Rachel Ward

  • Iterative thresholding algorithms

    Massimo Fornasier;Holger Rauhut

  • Quasi-orthogonal decompositions of structured frames

    Massimo Fornasier

  • Continuous Frames, Function Spaces, and the Discretization Problem

    Massimo Fornasier;Holger Rauhut

  • Numerical Methods for Sparse Recovery

    Massimo Fornasier

  • Mean-Field Optimal Control

    Massimo Fornasier;Francesco Solombrino

  • A Kinetic Flocking Model with Diffusion

    Renjun Duan;Massimo Fornasier;Giuseppe Toscani

  • Sparse stabilization and optimal control of the Cucker-Smale model

    Marco Caponigro;Massimo Fornasier;Benedetto Piccoli;Emmanuel Trélat

  • Theoretical foundations and numerical methods for sparse recovery

    Massimo Fornasier

  • Sparse Stabilization and Control of Alignment Models

    Marco Caponigro;Massimo Fornasier;Benedetto Piccoli;Emmanuel Trélat

  • Intrinsic localization of frames

    Massimo Fornasier;Karlheinz Gröchenig

  • Adaptive frame methods for elliptic operator equations

    Stephan Dahlke;Massimo Fornasier;Thorsten Raasch

  • Mean-field sparse optimal control

    Massimo Fornasier;Benedetto Piccoli;Francesco Rossi

  • An Introduction to Total Variation for Image Analysis

    Unknown

  • Compressive Sensing and Structured Random Matrices

    Massimo Fornasier

Frequent Co-Authors

Holger Rauhut
Holger Rauhut RWTH Aachen University
Ingrid Daubechies
Ingrid Daubechies Duke University
Stephan Dahlke
Stephan Dahlke Philipp University of Marburg
Carola-Bibiane Schönlieb
Carola-Bibiane Schönlieb University of Cambridge
Benedetto Piccoli
Benedetto Piccoli Rutgers, The State University of New Jersey
Emmanuel Trélat
Emmanuel Trélat Sorbonne University
Rachel Ward
Rachel Ward The University of Texas at Austin
Giuseppe Toscani
Giuseppe Toscani University of Pavia
Guust Nolet
Guust Nolet Université Côte d'Azur
Frederik J. Simons
Frederik J. Simons Princeton University

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