Weighted L∞ isotonic regression
نویسندگان
چکیده
منابع مشابه
Weighted L∞ isotonic regression
Algorithms are given for determining weighted L∞ isotonic regressions satisfying order constraints given by a directed acyclic graph (dag) with n vertices andm edges. An algorithm is given takingΘ(m log n) time for the general case. However, it relies on parametric search, so a practical approach is introduced, based on calculating prefix solutions. While not as fast in the general case, for li...
متن کاملStrict L∞ Isotonic Regression
Given a function f and weightsw on the vertices of a directed acyclic graphG, an isotonic regression of (f, w) is an order-preserving real-valued function that minimizes the weighted distance to f among all order-preserving functions. When the distance is given via the supremum norm there may be many isotonic regressions. One of special interest is the strict isotonic regression, which is the l...
متن کاملAlgorithms for L∞ Isotonic Regression
This paper gives algorithms for determining L∞ weighted isotonic regressions satisfying order constraints given by a DAG with n vertices and m edges. Throughout, topological sorting plays an important role. A modification to an algorithm of Kaufman and Tamir gives an algorithm taking Θ(m log n) time for the general case, improving upon theirs when the graph is sparse. When the regression values...
متن کاملWeighted isotonic regression under the L1 norm
Isotonic regression, the problem of finding values that best fit given observations and conform to specific ordering constraints, has found many applications in biomedical research and other fields. When the constraints form a partial ordering, solving the problem under the L1 error measure takes O(n) when there are n observations. The analysis of large-scale microarray data, which is one of th...
متن کاملL∞ Isotonic Regression for Linear, Multidimensional, and Tree Orders
Algorithms are given for determining L∞ isotonic regression of weighted data. For a linear order, grid in multidimensional space, or tree, of n vertices, optimal algorithms are given, taking Θ(n) time. These improve upon previous algorithms by a factor of Ω(log n). For vertices at arbitrary positions in d-dimensional space a Θ(n log n) algorithm employs iterative sorting to yield the functional...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computer and System Sciences
سال: 2018
ISSN: 0022-0000
DOI: 10.1016/j.jcss.2017.09.001