_index.org

opsin

Last edited: August 8, 2025

An opsin is a photo-receptor protein (sensitive to light) that is sensitive to light

Optimal Exploration Policy

Last edited: August 8, 2025

Suppose we have offline statistic regarding wins and losses of each slot machine as our state:

\begin{equation} w_1, l_{1}, \dots, w_{n}, l_{n} \end{equation}

What if we want to create a policy that maximises exploration?

We construct a value function:

\begin{equation} U^{*}([w_1, l_{1}, \dots, w_{n}, l_{n}]) = \max_{a} Q^{*}([w_1, l_{1}, \dots, w_{n}, l_{n}], a) \end{equation}

our policy is the greedy policy:

\begin{equation} U^{*}([w_1, l_{1}, \dots, w_{n}, l_{n}]) = \arg\max_{a} Q^{*}([w_1, l_{1}, \dots, w_{n}, l_{n}], a) \end{equation}

Optimal Stopping Problem

Last edited: August 8, 2025
  1. Shuffle cards
  2. Keep revealing cards
  3. “Stop” when there’s >50% chance the next card to be revealed is black

We can Frequentist Definition of Probability calculate the probability of a given card remaining is black:

\begin{equation} pblack(b,r) = \frac{26-b}{52-(r+b)} \end{equation}

now:

\begin{equation} pwin(b,r) = \begin{cases} 0, b+r = 52 \\ \max \qty[ \begin{align}&pblack(p,r), \\ &pblack(b,r)pwin(b+1,r) + (1-pblack(b,r)pwin(b, r+1) \end{align}] \end{cases} \end{equation}

“with the theory of the Martingales, this comes out to be 50%”

Optimization Index

Last edited: August 8, 2025

AA222/CS361

Applications

Example Objectives

  • efficiency
  • safety
  • accuracy

Example Constraints

  • cost
  • weight
  • (structural) integrity

Why this is hard?

  • high dimensional search spaces
  • multiple competing objectives
  • model uncertainty

Queen Dido’s Problem

  • you are granted a plot of land
  • how do we surround it with the smallest amount of string?

Design Workflow

“Optimization: how do we ‘change design’ in response to evaluation”

Logistics

aa222.stanford.edu

Overview

Lectures

Derivatives, Bracketing, Descent, and Approximations

Topics: Derivatives, Bracking, Descent, and Approximation

Optimizing Spark

Last edited: August 8, 2025

In the event your domain knowledge can help you make decisions about how spark load-balances or stripes data across worker nodes.

Persistence

“you should store this data in faster/slower memory”

MEMORY_ONLY, MEMORY_ONLY_SER, MEMORY_AND_DISK, MEMORY_AND_DISK_SER, DISK_ONLY

rdd.persist(StorageLevel.MEMORY_AND_DISK)
# ... do work ...
rdd.unpersist()

Parallel Programming