A special issue dedicated to Boris Polyak
Bibliographic record
Abstract
This special issue aims to honor Professor Boris Polyak's outstanding scientific contributions to the field of optimization by collecting some of the most recent advancements in optimization and its applications.During the last six decades, Professor Boris Polyak made fundamental contributions to various aspects of deterministic and stochastic optimization.Professor Boris Polyak is an original and brilliant mathematician, who put forth numerous novel ideas and concepts that strengthen all branches of optimization.His well-known book on optimization has educated generations of researchers, and his heavily cited papers provide a wealth of innovative ideas.This special issue is comprised of seven articles of high scientific merits whose contribution we summarise in the following:A. Agrawal, S. Barratt, S. Boyd, E. Busseti, and W. M. Moursi, in the paper entitled "Differentiating Through a Cone Program," consider the problem of efficiently computing the derivative of the solution map of a convex cone program.They implicitly differentiate the residual map for the homogeneous self-dual embedding and solve the linear systems of equations by employing an iterative method.This permits the authors to compute the derivative operator and its adjoint effectively.An open-source Python implementation of the new method solves a cone program and returns the derivative and its adjoint as abstract linear maps.The objective of the article entitled "A Henig conical regularization approach for circumventing the Slater conundrum in linearly ℓ p + -constrained least-squares problems" by L. Huerga, A. A. Khan and M. Sama is to explore a linearly ℓ p + -constrained least-squares problem.The authors develop a Henig conical regularization approach as a unified framework to deal with the lack of Slater-type constraint qualification.They provide some stability estimates for the regularized problems.For the separable case, p ∈ [1, ∞), they give an explicit characterization of the Henig dilating cones associated with ℓ p +
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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 | 0.005 |
| 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 | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.139 | 0.095 |
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".