COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Discrete Painlev equations and orthogonal polynomials

## Discrete Painlev equations and orthogonal polynomialsAdd to your list(s) Download to your calendar using vCal - van Assche, W (Katholieke Universiteit Leuven)
- Friday 12 July 2013, 14:00-14:30
- Seminar Room 1, Newton Institute.
If you have a question about this talk, please contact Mustapha Amrani. Discrete Integrable Systems It is now well known that the recurrence coefficients of many semi-classical orthogonal polynomials satisfy discrete and continuous Painlev equations. In the talk we show how to find the discrete Painlev equations by using compatibility relations or ladder operators. A combination with Toda type equations allows to find continuous Painlev equations. We have made an attempt to go through the literature and to collect all known examples of discrete (and continuous) Painlev equations for the recurrence coefficients of orthogonal polynomials. All input in completing the list is welcome. The required solutions usually satisfy some positivity constraint, leading to a unique solution with one boundary condition. Often the solution corresponds to solutions of the (continuous) Painlev equation in terms of special functions. This talk is part of the Isaac Newton Institute Seminar Series series. ## This talk is included in these lists:- All CMS events
- Featured lists
- INI info aggregator
- Isaac Newton Institute Seminar Series
- School of Physical Sciences
- Seminar Room 1, Newton Institute
- bld31
Note that ex-directory lists are not shown. |
## Other listsOne Day Meeting - 6th Annual Symposium of the Cambridge Computational Biology Institute BSS Internal Seminars Empowered Employability## Other talksReserved for CambPlants Cellular recycling: role of autophagy in aging and disease TBC Future directions panel Self-Assembled Nanomaterials for 3D Bioprinting and Drug Delivery Applications Bayesian deep learning |