# Presentation Structure

## List of all content

<!--
- (4) Background: Motivation
- (5) Background: Previous Work
- (6) Background: Presumptions & Notation
- (7) Background: Optimization vs graph based methods
- (9-11) Proximal: Theory
- (12) Proximal: Sim: Able to replicate literature
- (13) Proximal: Sim: Choice of gamma
- (14) Proximal: Sim: FER due to DFR
- (15-16) Proximal: Oscillation of estimate
- (17) Proximal: Improved: Emprirical FoM
- (18) Proximal: Improved: Algorithm
- (19) Proximal: Improved: Results
- (21-24) LP Decoding: General Theory
- (25-26) LP Decoding: ADMM Theory
- (27) LP Decoding: ADMM Sim: Able to replicate literature
- (28-29) LP Decoding: ADMM Sim: Choice of mu and rho
- (31) Comparison: Number of required iterations
- (32) Comparison: Time Complexity
- (33) Comparison: FER and BER
- (34) Comparison: FER and BER for more codes
- (35) Comparison: Algorithm structure
- (36) Comparison: Algorithm structure: Message passing
- (38) Conclusion and outlook
- (41) Supplementary slides: Choice of gamma for more codes
- (42) Supplementary slides: Choice of mu and rho for more codes
-->

| Slide | Content                                                    | TODO |
| ----- | ---------------------------------------------------------- | :--: |
| 4     | Background: Motivation                                     |  -   |
| 5     | Background: Previous Work                                  |  X   |
| 6     | Background: Presumptions & Notation                        |  -   |
| 7     | Background: Optimization vs graph based methods            |  -   |
| 9-11  | Proximal: Theory                                           |  -   |
| 12    | Proximal: Sim: Able to replicate literature                |  -   |
| 13    | Proximal: Sim: Choice of gamma                             |  X   |
| 14    | Proximal: Sim: FER due to DFR                              |  -   |
| 15-16 | Proximal: Oscillation of estimate                          |  -   |
| 17    | Proximal: Improved: Emprirical FoM                         |  -   |
| 18    | Proximal: Improved: Algorithm                              |  -   |
| 19    | Proximal: Improved: Results                                |  -   |
| 21-24 | LP Decoding: General Theory                                |  -   |
| 25-26 | LP Decoding: ADMM Theory                                   |  -   |
| 27    | LP Decoding: ADMM Sim: Able to replicate literature        |  -   |
| 28-29 | LP Decoding: ADMM Sim: Choice of mu and rho                |  S   |
| 31    | Comparison: Number of required iterations                  |  S   |
| 32    | Comparison: Time Complexity                                |  S   |
| 33    | Comparison: FER and BER                                    |  S   |
| 34    | Comparison: FER and BER for more codes                     |  S   |
| 35    | Comparison: Algorithm structure                            |  -   |
| 36    | Comparison: Algorithm structure: Message passing           |  X   |
| 38    | Conclusion and outlook                                     |  R   |
| 41    | Supplementary slides: Choice of gamma for more codes       |  -   |
| 42    | Supplementary slides: Choice of mu and rho for more codes  |  -   |
| -     | Current and new approaches for improving proximal decoging | TODO |

## Questions

- Is a notational slide that important in this case?
  - Yes, otherwise the listeners won't understand the following slides
- Should LP Decoding and ADMM even be mentioned?
  - Yes, the basis for the presentation is still the thesis. Also, the title of
    the presentation is "-- optimization --", not "-- proximal decoding --"
- Mention approaches not taken (e.g., Adam for proximal decoding)?
  - Yes: "Zur Debatte stellen"
- Present current state of research on improved algorithm (e.g., questions
  raised in last meeting with Jonathan)?
  - Yes, at least partly mention current approaches and insights not present in
    the thesis
- Compare proximal and LP decoding using ADMM?
  - Maybe at least mention that the general structure of the objective function
    is similar and why we would expect proximal decoding to work better and why
    it's surprising it doesn't.
- Present insights on parameter choice?
  - Maybe only in supplementary slides?
- Corroborate results with slides showing additional simulations for multiple
  codes?
  - Maybe only in supplementary slides?