Marathon training starts today

Haven’t posted much on my own running, but wanted to stay honest on this one.

Not me, but at least I took the photo.

Building up to the 2013 Austin Marathon, with a goal of sub-2:30, which should be good for a top-5 (top-1?) finish.  Training started today, after 2+ weeks entirely off, the first time I’ve managed that in quite a while.  Hoping that a 12 week focused buildup will work well.  My daily mileage is borrowed mostly from here, though the 90 mile week I will likely substitute 10-12-15-11-10-22-10 to keep the second long run down a little bit.

Additional workouts will be marathon pace (5:30-5:50) segments built into the long run, starting at ~30 minutes this week and building up to (hopefully) ~90 minutes.  These will be negative splits.

Wednesdays will be faster pieces, ~5 miles at 5:00-5:20/mile, broken up at first, and (hopefully) continuous by the end of the cycle.  I’ll bring the Wednesday workout in after two weeks of training.

Weekly mileages (tempo time, Wednesday workout):

  • Nov. 25 45mi (30mins)
  • Dec. 2 56mi (35mins)
  • Dec. 9 66mi (40mins, 5xmile, 2mins recover)
  • Dec. 16 78mi (45mins, 4 x 2km, 90s recover)
  • Dec. 23 66mi (50mins, 4 x 1.5mi, 90s recover)
  • Dec. 30 78mi (55mins, 3 x 3km, 60s recover)
  • Jan. 6 90mi (2 x 2.5mi, 60s recover, long run pushed back one day)
  • Jan. 13 78mi (3M half marathon the 13th, 70mins the 19th, 2 x 3mi, 30s recover)
  • Jan. 20 90mi (80mins, 5miles continuous)
  • Jan. 27 90mi (90mins, 2 x 3mi, 30s recover)
  • Feb. 3 78mi (60mins, 5miles continuous)
  • Feb. 10 70mi (no saturday long run, 3miles continuous, race sunday)



Arrogance Personified

Well, since my last post, I successfully defended (footage from after the defense), found a job as an analytics developer at a startup in Austin, TX, and even moved out here.  Feels great to be out of Houston and — despite the Texas summer — I’ve started playing outside again, running, climbing and biking.  I’ve somewhat competitive plans of doing well at next February’s Austin marathon, as well as some mountain trips further in the future…

For now though, having tons of fun at the job, playing with gigantic data sets, and learning all sorts of new things.  I’d expect more posts about manifold learning and extracting structure from large networks in the future, but for today a track and field video I’ve loved for a long time, featuring Bill McChesney, John Treacy and Steve Ovett.


Coming up on my defense, so no fun for a little while.  Hopefully I’ll be back in May with new, interesting (to me!) tidbits and adventures.  Happy trails!

Bouncing balls

Inspired, as usual, by Leonid’s recent post, I decided to first write a script that would mimic his.  After that, since I had all the numbers worked out, I wrote two more MATLAB programs: one that mimicked elastic collisions in 2-dimensions, and one that mimics them in 3.

In theory, you can specify the number of particles and their radius, as well as the mass, position, and initial velocity for each (I didn’t vectorize radius for some reason, so I cannot model balls of different sizes bouncing around).  However, in practice I just generate random vectors for each of these numbers.  The final aspect is that the domain I put the balls in was a pool table of 9 x 4.5 units, or 9 x 4.5 x 4.5 for the 3D version.  This was just to make calculating the reflecting angle easier when a ball hit the wall.

As with Leonid’s code, mine works by checking whether the next step will cause any collisions, then adjusting the velocity vector so that the collision didn’t happen (using conservation of momentum and kinetic energy).  This algorithm is not “smart” in the sense that by avoiding one collision, it might get pushed into a second collision which it does not detect, and if a particle gets going fast enough, it can reflect off a wall from a large distance (my time step is just 0.01).  You can spot this in some of the figures below.

Anyways, here are some of the outputs.  I did not go through the trouble of turning these into .gifs, but they play fairly smoothly.  What happens is I simulate N particles of varying masses and velocities bouncing around in a 2- or 3- dimensional box for T seconds, then plot the path of one of the particles.  The end position of all the particles, plus this path, is in each picture below (with the “tracked” particle colored in red).

4 particles for 20 seconds. Not many collisions... I can spot 3, I think.

40 particles bouncing for 20 seconds. This particle is involved in a few more collisions than last time (and looks to have been moving faster, too.

Now 40 particles for 20 seconds in 3d. A few more collisions, and it looks like it was going pretty fast in the middle there for a while.

400 particles for 20seconds. Starting to look more like the "random walk" of Robert Brown's pollen, though I would certainly have to mess with how heavy the particles were to more accurately model that.