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Gradient descent

Iterates move opposite the gradient; step-size and smoothness conditions control convergence.

xk+1=xk−αk∇f(xk)x_{k+1}=x_k-\alpha_k\nabla f(x_k)

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Optimization and Operations Research

Explore objectives and convexity; algorithms and constraints; discrete decisions and multiple objectives. Concepts and connections are available independently of video coverage.

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