Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
نویسندگان
چکیده
منابع مشابه
Algorithmic Problems in Network Communication
This thesis deals with algorithmic aspects of communication in networks. The main focus of the thesis is to find factors which can influence significantly the efficiency in performing a given communication task. When we approach a problem, the first thing we have to do in order to study it, is to find a good model. A model is good if it allows to remove all factors that are irrelevant and expos...
متن کاملPolynomial Equivalence Problems: Algorithmic and Theoretical Aspects
The Isomorphism of Polynomial (IP) [27], which is the main concern of this paper, originally corresponds to the problem of recovering the secret key of a C∗ scheme [26]. Besides, the security of various other schemes (e.g. signature, authentication [27], traitor tracing [5], etc. . . ) also depends on the practical hardness of IP. Due to its numerous applications, the Isomorphism of Polynomial ...
متن کاملPolynomial Equivalence Problems: Algorithmic and Theoretical Aspects
The Isomorphism of Polynomials (IP) [28], which is the main concern of this paper, originally corresponds to the problem of recovering the secret key of a C∗ scheme [26]. Besides, the security of various other schemes (signature, authentication [28], traitor tracing [5], . . . ) also depends on the practical hardness of IP. Due to its numerous applications, the Isomorphism of Polynomials is thu...
متن کاملTheoretical and Algorithmic Solutions for Null Models in Network Theory
2 Generation of complex networks with given characteristics 17 2. 3 Contents 4 4 Null model for co-expression network based on Pearson correlation 69 4. Introduction The graph-theoretical based formulation for the representation of the data-driven structure and the dynamics of complex systems is rapidly imposing as the paramount paradigm [1] across a variety of disciplines, from economics to ne...
متن کاملAlgorithmic improvements for variational inference
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially useful when applying variational Bayes (VB) to models outside the conjugate-exponential family. For them, variational Bayesian expectation maximization (VB EM) algorithms are not easily available, and gradient-based m...
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 the ACM
سال: 1972
ISSN: 0004-5411,1557-735X
DOI: 10.1145/321694.321699