Difference between revisions of "User:Bobbywang"

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== About Me ==
 
== About Me ==
 
I'm currently a freshman at [http://www.duke.edu Duke University]. I will probably be an ECE/Computer Science double major.  
 
I'm currently a freshman at [http://www.duke.edu Duke University]. I will probably be an ECE/Computer Science double major.  
 
  
 
== Name Pronunciation ==
 
== Name Pronunciation ==
 
My name is pretty standard in terms of pronunciation - I pronounce it BAWH-bee WAYng.
 
My name is pretty standard in terms of pronunciation - I pronounce it BAWH-bee WAYng.
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==Grand Challenge Article==
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[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 ==
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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).

Latest revision as of 03:03, 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).