Difference between revisions of "User:Bobbywang"
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==Grand Challenge Article== | ==Grand Challenge Article== | ||
[http://iopscience.iop.org/1742-6596/244/1/012007 Grand challenges of inertial fusion energy], J H Nuckolls, IOP Science, 2010, 24 Jan. 2015 (Provide energy from fusion) | [http://iopscience.iop.org/1742-6596/244/1/012007 Grand challenges of inertial fusion energy], J H Nuckolls, IOP Science, 2010, 24 Jan. 2015 (Provide energy from fusion) | ||
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| + | == Favorite MATLAB Demonstration == | ||
| + | My favorite MATLAB demonstration was the traveling salesman demonstration. This is a problem that I had read a little bit about before, but never experienced interactively. I thought it was really cool to see how the trip got shorter and shorter as time went on, and how a small increase the number of cities greatly increased the amount of time required to find an optimal path. I actually ended up reading some more about the traveling salesman problem after trying out the app, and I'm curious as to what method they used to find the shortest path - a simple greedy algorithm, or something more complicated (such as simulated annealing). | ||
Revision as of 03:02, 25 January 2015
About Me
I'm currently a freshman at Duke University. I will probably be an ECE/Computer Science double major.
Name Pronunciation
My name is pretty standard in terms of pronunciation - I pronounce it BAWH-bee WAYng.
Grand Challenge Article
Grand challenges of inertial fusion energy, J H Nuckolls, IOP Science, 2010, 24 Jan. 2015 (Provide energy from fusion)
Favorite MATLAB Demonstration
My favorite MATLAB demonstration was the traveling salesman demonstration. This is a problem that I had read a little bit about before, but never experienced interactively. I thought it was really cool to see how the trip got shorter and shorter as time went on, and how a small increase the number of cities greatly increased the amount of time required to find an optimal path. I actually ended up reading some more about the traveling salesman problem after trying out the app, and I'm curious as to what method they used to find the shortest path - a simple greedy algorithm, or something more complicated (such as simulated annealing).
