Astral Space
Convex Analysis at Infinity
$149.09
- Hardcover
656 pages
- Release Date
1 December 2026
Summary
From three of today’s top researchers in machine learning, a groundbreaking new theory for understanding convex minimization at infinity
Numerous fields of study rely on methods for minimizing convex functions. Not all convex functions, however, have finite minimizers; some can only be minimized by a sequence as it heads to infinity, making it considerably more challenging to prove correct convergence to a minimizer.
This book develops an expansive new theory …
Book Details
| ISBN-13: | 9780691261126 |
|---|---|
| ISBN-10: | 0691261121 |
| Author: | Robert E. Schapire, Miroslav Dudík, Matus Telgarsky |
| Publisher: | Princeton University Press |
| Imprint: | Princeton University Press |
| Format: | Hardcover |
| Number of Pages: | 656 |
| Release Date: | 1 December 2026 |
| Dimensions: | 254mm x 178mm |
Robert E. Schapire
Miroslav Dudk is senior principal research manager at Microsoft Research in New York City and cofounder of Fairlearn, an open-source project for algorithmic fairness.
Robert E. Schapire is partner researcher at Microsoft Research in New York City and the coauthor (with Yoav Freund) of Boosting: Foundations and Algorithms.
Matus Telgarsky is associate professor at the Courant Institute School of Mathematics, Computing, and Data Science at New York University.
Returns
This item is eligible for free returns within 30 days of delivery. See our returns policy for further details.