# Bidding Algorithms Benchmark

> Complete comparison framework for real-time bidding (RTB) algorithms in online advertising.
> Optimizing for clicks under budget constraints using Lagrangian dual methods.

## Research Resources

- **[RESEARCH_RESOURCES.md](RESEARCH_RESOURCES.md)** — Full literature survey: 32 papers across bidding algorithms, CTR prediction, and clearing price models
- **[AUDIT_TRAIL.md](AUDIT_TRAIL.md)** — Every paper, dataset, codebase, and external resource consulted (44 items)

## Problem Setup

- **Objective**: Maximize number of clicks
- **Constraints**: Total spend ≤ Budget, with k% minimum spend guarantee
- **Auction Types**: First-price and second-price
- **Core Approach**: Lagrangian dual multiplier with online error gradient descent

## Algorithms

| Algorithm | Type | Auction | Paper |
|-----------|------|---------|-------|
| **DualOGD** | Adaptive | First-price | Wang et al. 2023 [2304.13477] |
| **DualMirrorDescent** | Adaptive | Second-price | Balseiro et al. 2020 [2011.10124] |
| **DualRoS** | Adaptive | Second-price | Feng et al. 2022 [2208.13713] |
| **TwoSidedDual** | Adaptive | First-price | Extension (cap + floor) |
| **RLB** | RL+MDP | Both | Cai et al. 2017 [1701.02490] |
| **Linear** | Static | Both | Baseline |
| **ORTB** | Static | Second-price | Zhang et al. 2014 (KDD) |

## Models

| Model | Task | Architecture | Dataset |
|-------|------|-------------|---------|
| **FinalMLP** | CTR Prediction | Two-stream MLP + Feature Gating | Criteo_x4 |
| **DeepFM** | CTR Prediction | FM + DNN (baseline) | Criteo_x4 |
| **TorchSurv** | Clearing Price | Deep Survival (Cox PH) | Simulated |
| **EmpiricalCDF** | Win Probability | Non-parametric | Online |

## Structure

```
bidding_algorithms_benchmark/
├── README.md
├── RESEARCH_RESOURCES.md
├── AUDIT_TRAIL.md
├── src/
│   ├── ctr/
│   │   ├── train_finalmlp.py
│   │   └── train_deepfm.py
│   ├── price/
│   │   ├── empirical_cdf.py
│   │   └── torchsurv_model.py
│   ├── algorithms/
│   │   ├── dual_ogd.py
│   │   ├── dual_mirror_descent.py
│   │   ├── dual_ros.py
│   │   ├── two_sided_dual.py
│   │   ├── rlb.py
│   │   └── baselines.py
│   └── benchmark/
│       ├── auction_simulator.py
│       ├── run_comparison.py
│       └── sweep.py
├── configs/
│   ├── finalmlp_criteo.yaml
│   └── sweep_config.yaml
├── results/
└── requirements.txt
```