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GATE 2026 · session-8
Calculus and OptimizationOptimization Involving a Single VariablemediumNAT1 mark
Consider that for a supervised learning task, the objective function being minimized is \(f_w(x) = wx\), where \(x \in \mathbb{R}\) is the input and \(w \in \mathbb{R}\) is the parameter. Stochastic Gradient Descent with a learning rate of 0.10 is used for parameter updates. Suppose that at the end of iteration \(i\), the value of \(w\) becomes 10.00. Let \(x = 10.00\) be the input for iteration \((i+1)\). The value of \(w\) at the end of iteration \((i+1)\) is __________. (Rounded off to two decimal places)
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