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ml-recall

#tangent

1 question from Optimization & Calculus

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Question 1: Short answer

mediumAI-checkedOptimization & Calculus

Forward-mode automatic differentiation evaluates f(x1,x2)=ln⁡x1+x1x2f(x_1, x_2) = \ln x_1 + x_1 x_2 at (x1,x2)=(e2,π)(x_1, x_2) = (e^2, \pi) through the trace v1=ln⁡x1v_1 = \ln x_1, v2=x1x2v_2 = x_1 x_2, f=v1+v2f = v_1 + v_2, carrying a tangent v˙\dot v along with every value. With the seeds x˙1=1\dot x_1 = 1 and x˙2=0\dot x_2 = 0, what tangent f˙\dot f comes out? Round to 3 decimals.

a number, 3 decimals