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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Newton optimization
Gradient and Hessian information form a local quadratic step, often with globalization safeguards.
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Gradient descent
Iterates move opposite the gradient; step-size and smoothness conditions control convergence.
Linear programming model
Maximise or minimise a linear objective subject to linear inequalities, the workhorse of allocation problems.
Convex functions
A convex function lies below chords connecting points on its graph.
Convex optimization
Minimizing a convex function on a convex feasible set makes local minima global.
Convex sets
Every segment joining two points of a convex set stays inside the set.
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- Gradient descent · Related · Map context
- Linear programming model · Related · Map context