Cost Aware BO Tutorial (#1922)

Summary:
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## Motivation

To add a tutorial about cost aware bayesian optimization with cost cooling, which was a paper in the AutoML workshop at ICML in 2020. A few people had been asking me for code and I thought it would be nice to have something in Botorch.

https://arxiv.org/pdf/2003.10870.pdf

The tutorial covers how to define a cost model, how to define expected improvement per unit using this cost model (expected improvement normalized by the cost model, with an additional decay factor to prevent evaluation of too many cheap points). It then runs through a fun little problem about noisy least squares estimation and shows the benefit of this cost-aware approach. It also stresses the importance of using an accurate cost model if you have a reasonable idea of what it ought to look like (as we do with least squares estimation).

### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?

Yes I have, and I have signed the open source agreement.

Pull Request resolved: #1922

Test Plan:
No new testing required.

## Related PRs

No related PRs.

Reviewed By: Balandat

Differential Revision: D48421374

Pulled By: dme65

fbshipit-source-id: 24b453e5e2e15d6854d9d3a7ca7eeef03b6ac2f0

Author: erichanslee

tutorials/cost_aware_bayesian_optimization.ipynb

@@ -38,6 +38,10 @@
       "id": "saasbo",
       "title": "High-dimensional Bayesian optimization with SAASBO"
     },
+    {
+      "id": "cost_aware_bayesian_optimization",
+      "title": "Cost-aware Bayesian optimization"
+    },
     {
       "id": "Multi_objective_multi_fidelity_BO",
       "title": "Multi-Objective-Multi-Fidelity optimization with MOMF"