Does anyone use this potential alternative to gradient descent?

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[ Step 1: there is some loss\/cost function but we don't know its optimal parameters ](https://preview.redd.it/vo2fb58taouc1.png?width=723&format=png&auto=webp&s=d700eadb8435238bcf549c71cf7974d0d1d27cc1)

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[ Step 2: solve for the derivatives at random points for the parameters and obtain tangent vectors for those points. ](https://preview.redd.it/td8kuu9waouc1.png?width=619&format=png&auto=webp&s=5e0d879dda0ebb502895350fc23a302393268f74)

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[ Step 3: Solve for where the vectors \\"cross\\" \(when stretched\) in terms of the parameters, and plug those parameters into the loss function. If it seems to be a good place, you could try gradient descent\/back-prop starting from here. …

datascience dimensions easy example gradient people think