A special issue dedicated to Christiane Tammer
Bibliographic record
Abstract
Scalarization techniques are at the core of the theoretical as well as numerical developments in vector and set-valued optimization.Christiane Tammer holds a special place in the advancement of scalarization techniques as she strengthened one of the most influential classes of nonlinear scalarization functionals and applied it to explore various aspects of optimization.This special issue aims to honor Christiane Tammer's valuable contributions to the critical field of nonlinear scalarization by collecting some of the most recent advancements in scalarization techniques and their numerous applications in vector optimization, set optimization, and robust optimization.This special issue is comprised of ten articles of excellent scientific quality whose contribution is summarized in the following: B. Mordukhovich and N. M. Nam, in the interesting article entitled "The Fermat-Torricelli Problem and Weiszfeld's Algorithm in the Light of Convex Analysis," investigate the celebrated Fermat-Torricelli problem from both theoretical and algorithmic viewpoints by employing the powerful machinery of convex analysis and optimization.The objective of the paper entitled "Variational Principles in Set Optimization with Domination Structures and Application to Changing Jobs" by T. Q. Bao and A. Soubeyran is to propose and analyze new versions of Ekeland's variational principle in set optimization with domination structure.The authors use Gerstewitz's nonlinear scalarization function to convert a set-valued map into an extended real-valued function and the idea of the proof of Dancs-Hegedus-Medvegyev's fixed-point theorem.The developed framework applies to dynamic processes of changing jobs where the cost function does not satisfy the symmetry axiom of metrics and the class of set-valued maps acting from a quasi-metric space into a real linear space.A. Löhne, D. Dörfler, A. Rittmann, and B. Weißing in the contribution entitled "Solving Bilevel Problems with Polyhedral Constraint Set," investigate the relationship between bilevel programs and polyhedral projection problems.They generalize a result by Fulop (1993) to show that solving a bilevel problem with polyhedral constraints is equivalent to optimizing the upper-level objective over certain facets of an associated polyhedral projection problem.Based on this result, they develop an algorithmic framework to compute solutions of bilevel problems and provide convincing numerical examples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.145 | 0.106 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".