Tuomo Valkonen

906 total citations
39 papers, 413 citations indexed

About

Tuomo Valkonen is a scholar working on Computational Mechanics, Mathematical Physics and Computational Theory and Mathematics. According to data from OpenAlex, Tuomo Valkonen has authored 39 papers receiving a total of 413 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computational Mechanics, 13 papers in Mathematical Physics and 11 papers in Computational Theory and Mathematics. Recurrent topics in Tuomo Valkonen's work include Sparse and Compressive Sensing Techniques (15 papers), Numerical methods in inverse problems (12 papers) and Advanced Optimization Algorithms Research (9 papers). Tuomo Valkonen is often cited by papers focused on Sparse and Compressive Sensing Techniques (15 papers), Numerical methods in inverse problems (12 papers) and Advanced Optimization Algorithms Research (9 papers). Tuomo Valkonen collaborates with scholars based in Finland, United Kingdom and Ecuador. Tuomo Valkonen's co-authors include Carola‐Bibiane Schönlieb, Kristian Bredies, Florian Knöll, Juan Carlos De los Reyes, Dirk A. Lorenz, Jan Lellmann, Karl Kunisch, Martin Benning, Lynn F. Gladden and Daniel J. Holland and has published in prestigious journals such as Journal of Mathematical Analysis and Applications, SIAM Journal on Applied Mathematics and Nonlinear Analysis.

In The Last Decade

Tuomo Valkonen

37 papers receiving 387 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tuomo Valkonen Finland 9 196 170 115 70 66 39 413
Frank Lenzen Germany 8 184 0.9× 205 1.2× 169 1.5× 60 0.9× 109 1.7× 17 532
Alessandro Buccini Italy 10 149 0.8× 88 0.5× 184 1.6× 61 0.9× 23 0.3× 42 324
Caroline Chaux France 7 222 1.1× 181 1.1× 64 0.6× 28 0.4× 51 0.8× 13 354
Xiao-Guang Lv China 17 314 1.6× 522 3.1× 67 0.6× 73 1.0× 46 0.7× 52 769
Vincent Duval France 11 368 1.9× 374 2.2× 141 1.2× 51 0.7× 97 1.5× 24 735
Shai Dekel Israel 13 133 0.7× 102 0.6× 73 0.6× 23 0.3× 32 0.5× 36 382
Bryan W. Lewis United States 9 108 0.6× 84 0.5× 238 2.1× 174 2.5× 21 0.3× 17 397
Triet Le United States 9 176 0.9× 463 2.7× 97 0.8× 35 0.5× 66 1.0× 17 636
Валентина Станева United States 4 372 1.9× 150 0.9× 53 0.5× 22 0.3× 60 0.9× 10 484
Silvia Bonettini Italy 13 437 2.2× 240 1.4× 185 1.6× 119 1.7× 106 1.6× 47 704

Countries citing papers authored by Tuomo Valkonen

Since Specialization
Citations

This map shows the geographic impact of Tuomo Valkonen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tuomo Valkonen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tuomo Valkonen more than expected).

Fields of papers citing papers by Tuomo Valkonen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tuomo Valkonen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tuomo Valkonen. The network helps show where Tuomo Valkonen may publish in the future.

Co-authorship network of co-authors of Tuomo Valkonen

This figure shows the co-authorship network connecting the top 25 collaborators of Tuomo Valkonen. A scholar is included among the top collaborators of Tuomo Valkonen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tuomo Valkonen. Tuomo Valkonen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Valkonen, Tuomo, et al.. (2024). Predictive online optimisation codes for dynamic inverse imaging problems. Zenodo (CERN European Organization for Nuclear Research). 2 indexed citations
2.
Jauhiainen, J., et al.. (2024). Prediction Techniques for Dynamic Imaging with Online Primal–Dual Methods. Journal of Mathematical Imaging and Vision. 66(6). 1109–1134.
3.
Valkonen, Tuomo. (2023). Proximal methods for point source localisation. Volume 4(Original research articles). 1 indexed citations
4.
Valkonen, Tuomo, et al.. (2023). Linearly convergent bilevel optimization with single-step inner methods. Computational Optimization and Applications. 87(2). 571–610. 4 indexed citations
5.
Jauhiainen, J., Aku Seppänen, & Tuomo Valkonen. (2022). Mumford–Shah regularization in electrical impedance tomography with complete electrode model. Inverse Problems. 38(6). 65004–65004. 6 indexed citations
6.
Valkonen, Tuomo. (2021). Codes for "Regularisation, optimisation, subregularity". Zenodo (CERN European Organization for Nuclear Research). 6 indexed citations
7.
Valkonen, Tuomo. (2020). Codes for "Predictive online optimisation with applications to optical flow". Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
8.
Clason, Christian, Stanislav Mazurenko, & Tuomo Valkonen. (2020). Codes for "Primal–dual proximal splitting and generalized conjugation in non-smooth non-convex optimization". Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
9.
Clason, Christian, Stanislav Mazurenko, & Tuomo Valkonen. (2020). Primal–Dual Proximal Splitting and Generalized Conjugation in Non-smooth Non-convex Optimization. Applied Mathematics & Optimization. 84(2). 1239–1284. 9 indexed citations
10.
Mazurenko, Stanislav, J. Jauhiainen, & Tuomo Valkonen. (2019). Diffusion tensor imaging codes from "Primal-dual block-proximal splitting for a class of non-convex problems". Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
11.
Valkonen, Tuomo. (2017). Preconditioned Proximal Point Methods and Notions of Partial Subregularity. 28(1). 251–278. 3 indexed citations
12.
Valkonen, Tuomo. (2017). The jump set under geometric regularisation. Part 2: Higher-order approaches. Journal of Mathematical Analysis and Applications. 453(2). 1044–1085. 6 indexed citations
13.
Benning, Martin, Lynn F. Gladden, Daniel J. Holland, Carola‐Bibiane Schönlieb, & Tuomo Valkonen. (2013). Phase reconstruction from velocity-encoded MRI measurements – A survey of sparsity-promoting variational approaches. Journal of Magnetic Resonance. 238. 26–43. 39 indexed citations
14.
Valkonen, Tuomo, Kristian Bredies, & Florian Knöll. (2013). Total Generalized Variation in Diffusion Tensor Imaging. SIAM Journal on Imaging Sciences. 6(1). 487–525. 91 indexed citations
15.
Valkonen, Tuomo, Kristian Bredies, & Florian Knöll. (2013). TGV for diffusion tensors: A comparison of fidelity functions. Journal of Inverse and Ill-Posed Problems. 21(3). 355–377. 4 indexed citations
16.
Bredies, Kristian, Karl Kunisch, & Tuomo Valkonen. (2012). Properties ofL1-TGV2: The one-dimensional case. Journal of Mathematical Analysis and Applications. 398(1). 438–454. 29 indexed citations
17.
Valkonen, Tuomo & Tommi Kärkkäinen. (2010). Clustering and the perturbed spatial median. Mathematical and Computer Modelling. 52(1-2). 87–106. 1 indexed citations
18.
Valkonen, Tuomo. (2010). Transport equation and image interpolation with SBD velocity fields. Journal de Mathématiques Pures et Appliquées. 95(5). 459–494. 3 indexed citations
19.
Valkonen, Tuomo. (2009). Optimal transportation networks and stations. Interfaces and Free Boundaries Mathematical Analysis Computation and Applications. 11(4). 569–597. 1 indexed citations
20.
Valkonen, Tuomo. (2008). Diff-convex combinations of Euclidean distances: a search for optima. Jyväskylä University Digital Archive (University of Jyväskylä). 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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