_index.org

Driving

Last edited: June 6, 2026

Gah I have to do this. Not for public consumption. California laws 2022 DL600 R7 2022.

Consequences

Not licensed

  • If unlicensed person is drivnig your car, it maybe impounded for 30 days
  • Hired to drive interstate commercially need to be older than 21, also need to be older than 21 to transport hazardous materials

Class C License

Driving #knw

  • Two axle vehicle with a GVWL of 26,000 lbs or less
  • Three axle vehicle weighing 6,000 lbs or less
  • House car < 40 feet or less
  • Three wheel motocycles
  • Vanpool vehicle designed to carry between 10 and no more than 15 people

Towing #knw

  • Single vehicle of 10,000 or less
  • Vehicle weighing 4000 lbs or more unladen
    • Trailer coach under 10,000 lbs
    • Fifth wheel trailer exceeding 10,000 lbs but under 15,000 lbs, with endorsement

Mor ethings

  • Class C drivers can’t tow more than one
  • Motor vehile weigning under 4000 lbs cannot tow more than 6000 lbs

Getting in trouble

  • Get a traffic ticket and fail to show up to court: suspend driving
  • One at fault collision or one at fault traffic violation: may take action?
  • Two of either at fault collision or violation conviction: no driving for 30 days unless accompanied by 25 year old adult
  • Three of “”: no driving for 6 months, on probation for a year.
  • Drugs or alcohol between 13-21: suspension for a year

Minor driving

Not sure if this applies

Linear Constraint Optimization

Last edited: June 6, 2026

\begin{align} \min_{x}\ &c^{\top} x + d \\ s.t.\ &Gx \preceq h \\ & Ax = b \end{align}

  • linear objective function
  • linear constraints

single our inequality forms a half-space; the entire feasible set is denoted by a series of linear functions—-these linear equalities are each CONVEX. The resulting feasible set, then, is ALSO convex—-meaning any line within the set remains within the set. So, any local minimum is a global minimum.

This is a convex problem where all constrains and objectives are affine.

NUS-ECON320 Volatility Hedging

Last edited: June 6, 2026

Let \(X\) denote price and \(Y\) denote volatility. The two objects obey the following process:

\begin{equation} \begin{cases} \dd{X} = \mu X \dd{t} + XY \dd{W} \\ \dd{Y} = \sigma Y \dd{B} \end{cases} \end{equation}

where, \(W\) and \(B\) are correlated Brownian motions with correlation \(\rho\) — \(E[(\dd{W})(\dd{B})] = \rho \dd{t}\).

Let’s work with \(Y\) first. We understand that \(Y\) is some continuous variable \(e^{a}\). Therefore, \(\dv{Y}{t}=ae^{a}\). Therefore, \(dY = ae^{a}dt\). Finally, then \(\frac{\dd{Y}}{Y} = \frac{ae^{a}}{e^{a}}\dd{t} = a\).

verefiable computation

Last edited: June 6, 2026

Geology Index

Last edited: June 6, 2026

Lectures