Editorial: A special issue on optimization and related topics dedicated to Professor Roman Polyak
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Roman Polyak's debut in Optimization goes back to the early 60-s, when in collaboration with S. Zuchovitsky and M. Primak he developed, independently on G. Zoutendijk, the method of feasible direction in both Euclidian and Gilbert spaces. In the 60-s, they solved the standardization problem, which leads to the minimization of a concave function on a special poletop. Their method finds the global minimum in polynomial time. In the late 60-s and early 70-s, they developed several methods for finding Nash equilibrium in n-person concave game and the Walras-Wald equilibrium. In the mid-60-s, Roman developed primal-dual methods for convex optimization. In the 80-s, Roman developed the Nonlinear Rescaling (NR) theory and exterior point methods for constrained optimization. The NR theory allows to eliminate the basic drawbacks of the classical Sequential Unconstrained Minimization Technique for Nonlinear Programming (NLP). In particular, his Modified Barrier Functions methods had been used with great success for solving large scale real life NLP problems, including planning radiation therapy, truss topology design, optimal power flow and antenna design. Numerical realization of NR methods requires efficient tools for unconstrained optimization, therefore, Roman introduced Regularized Newton method, established its global convergence for any strictly convex function, which has a minimizer, proved local quadratic convergence and estimated its complexity bound. The NR theory has become the foundation for PENNON -one of the best NLP solvers. Together with his former PhD student Igor Griva, he developed Primal-Dual NR theory
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it