A Two-Line Algorithm for Provingq-Hypergeometric Identities
β Scribed by Lily Yen
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 206 KB
- Volume
- 213
- Category
- Article
- ISSN
- 0022-247X
No coin nor oath required. For personal study only.
β¦ Synopsis
We show that q-hypergeometric identities Γ F n, k s 1 can be proved by k checking that they are correct for only finitely many, N say, values of n. We give a specific a priori formula for N, as a polynomial of degree 24 in the parameters of Ε½ . F n, k . We see this because of the presence of ''q'', the estimates of N can be made smaller than the general estimates that were found in the author's thesis Ε½''Contributions to the Proof Theory of Hypergeometric Identities,'' pp. 1α83, . Ph.D. thesis, University of Pennsylvania, Philadelphia, 1993 . As an example of the method we show that the q-Vandermonde identity can be proΒ¨ed by ''only'' Ε½ . checking that its first 2358 cases i.e., values of n are correct, by direct computation.
π SIMILAR VOLUMES
In this paper we present a new analysis of two algorithms, Gradient Descent and Exponentiated Gradient, for solving regression problems in the on-line framework. Both these algorithms compute a prediction that depends linearly on the current instance, and then update the coefficients of this linear
## Abstract Force field based energy minimization of molecular structures is a central task in computational chemistry and biology. Solving this problem usually requires efficient local minimization techniques, i.e., iterative twoβstep methods that search first for a descent direction and then try
Given a collection I I of n jobs that are represented by intervals, we seek a maximal feasible assignment of the jobs to k machines such that not more than Ε½ . c M intervals overlap pairwise on any machine M and that a job is only assigned to a machine if it fits into one of several prescribed time
In this note the recent algorithm of Hassan and Singh is modified to provide a more powerful approach to the hierarchical optimisation of non-linear systems with quadratic performance indices. The new approach does not use the quadratic penalty terms in the cost function. This allows convergence ove