SU-SOC175 FEB232026
Last edited: February 2, 2026Implications: rapid population aging. 1-child policy means that cost of pensions, health care, and social services will thus rise.
- 1 in 6 now above 60
- 1 in 4 above 2030
- 1 in 3 by 2050
“get old before getting rich”
demography
1980-2010
- low dependency ratios
- high household savings rate
- large lavor supply
- “age of abundance”
decline
- rapidly aging population
- rapidly rising dependency ratios
- ageing population / rising health / retirement costs
- shrikning labor force, rising wages
- declining of deposits in bank system
- less capital
resulting dynamics
- labor force shrinking by 2 million workers per year
- manufacturing wages rising faster
- labor cost advantage almost gone
wages keep going up
Knee Socks
Last edited: February 2, 2026You got the lights on in the afternoon
And the nights are drawn out long
And you’re kissin’ to cut through the gloom
With a cough drop coloured tongue
And you were sittin’ in the corner with the coats all piled high
And I thought you might be mine
In a small world, on an exceptionally rainy Tuesday night
In the right place and time
When the zeros line up on the 24 hour clock
centering
Last edited: February 2, 2026Given a convex set, we want to find the “center” of this set.
- Chebyshev center; this is not unique
- center of the maximum volume inscribing ellispsoid—this is also affine invariant
minimum volume ellipsoid
Last edited: February 2, 2026Lowner-John Ellipsoid
minimum volume surrounding ellipsoid
Consider a set of ellipsoid \(C\). Minimum volume ellipsoid \(\epsilon\) with \(C \subset \epsilon\). We can parameterize \(\epsilon\) as \(\epsilon = \qty {v \mid \norm{Av + b}_{2} \leq 1}\); where we assume \(A \in \mathcal{S}_{++}^{n}\).
The volume is proportional to \(\text{det} A^{-1}\). Thus to find minimal-volume ellipsoid, solve:
\begin{align} \min_{A,b}\quad & \log \text{det} A^{-1} \\ \textrm{s.t.} \quad & \text{sup}_{v \in C} \norm{A v + b}_{2} \leq 1 \end{align}
relax and round
Last edited: February 2, 2026In convex optimization, relax and round / polishing is a procedure by which you perform a local search after coming up with a relaxation, and round into the actual feasible set (such as integers).
