Konferenzen zum Thema Numerische Analysis in Mexiko

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1
Workshop — Outstanding Challenges in Computational Methods for Integral Equations
31. Mai 2020 - 05. Jun 2020 • Oaxaca, Mexiko
Veranstalter:
BIRS-affiliated mathematics research centre, Casa Matemática Oaxaca (CMO)
Zusammenfassung:
As technology advances, so does the cost (both time and energy) of experimental design. For certain classes of problems (e.g. radar propagation, fluid flow, heat dispersion, structural modeling), in which the observed phenomena is well-understood and accurately modelled via the equations of classical mathematical physics, design by simulation can mitigate some of the costs associated with purely experimental design. However, in order to do this, advances in both the mathematical formulation of the problem and the computational algorithms used in their solution are needed. This workshop brings together experts in various fields of mathematics and computation to develop next-generation computational tools and algorithms that will bridge the gap between design by experiment and design by simulation.
Eintrags-ID:
1330168
2
Workshop — Theory and Computational Methods for SPDEs
06. Sep 2020 - 11. Sep 2020 • Oaxaca, Mexiko
Veranstalter:
BIRS-affiliated mathematics research centre, Casa Matemática Oaxaca (CMO)
Zusammenfassung:
The BIRS workshop on theory and computational methods for SPDEs brings together a number of world experts in the theoretical analysis of these equations, which model dynamical systems disturbed by noise together with specialists in scientific computing, in a first meeting of its kind.
Eintrags-ID:
1330181
3
Workshop — Approximation Algorithms and the Hardness of Approximation
20. Sep 2020 - 25. Sep 2020 • Oaxaca, Mexiko
Veranstalter:
BIRS-affiliated mathematics research centre, Casa Matemática Oaxaca (CMO)
Zusammenfassung:
Most of the many discrete optimization problems arising in the sciences, engineering, and mathematics are NP-hard, that is, there exist no efficient algorithms to solve them to optimality, assuming the P.not.=NP conjecture. The area of approximation algorithms focuses on the design and analysis of efficient algorithms that find solutions that are within a guaranteed factor of the optimal one. Loosely speaking, in the context of studying algorithmic problems, an approximation guarantee captures the quality of an algorithm -- for every possible set of input data for the problem, the algorithm finds a solution whose cost is within this factor of the optimal cost. A hardness threshold indicates the difficulty of the algorithmic problem -- no efficient algorithm can achieve an approximation guarantee better than the hardness threshold assuming that P.not.=NP. Over the last two decades, there have been major advances on the design and analysis of approximation algorithms, and on the complementary topic of the hardness of approximation.
Eintrags-ID:
1330201
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Stand vom 13. Februar 2020