Bibliographic record
Abstract
"Preface to the Second Edition Both the format and guiding vision of Handbook of Linear Algebra remain unchanged, but a substantial amount of new material has been included in the second edition. The length has increased from 1400 pages to 1900 pages. There are 20 new chapters. Subjects such as Schur complements, special types of matrices, generalized inverses, matrices over nite elds, and invariant subspaces are now treated in separate chapters. There are additional chapters on applications of linear algebra, for example, to epidemiology. There is a new chapter on using the free open source computer mathematics system Sage for linear algebra, which also provides a general introduction to Sage. Additional surveys of currently active research topics such as tournaments are also included. Many of the existing articles have been revised and updated, in some cases adding a substantial amount of new material. For example, the chapters on sign pattern matrices and on applications to geometry have additional sections. As was true in the rst edition, the topics range from the most basic linear algebra to advanced topics including background for active research areas. In this edition, many of the chapters on advanced topics now include Conjectures and Open Problems, either as a part of some sections or as a new section at the end of the chapter. The conjectures and questions listed in such sections have been in the literature for more than ve years at the time of writing, and often a number of partial results have been obtained. In most cases, the current (at the time of writing) state of research related to the question is summarized as facts. Of course, there is no guarantee that (years after the writing date) such problems have not been solved (in fact, we hope they ha"--
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".