Course

Continuous optimization

Course under construction · 12 of 35 concepts published; more are added as they are reviewed.

You learn the foundations of continuous optimization: convexity, optimality conditions, gradient descent, Newton and quasi-Newton methods, constraints, duality, linear programming and proximal methods, tools for solving minimization problems in science and engineering. It helps to have taken Linear Algebra and Multivariable Calculus.

Recommended before this course: Linear algebra · Multivariable calculus (5 concepts come from those courses and are tagged)

17concepts
5topics
170exercises
≈ 9 hof study approx.

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Course path

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Sources

  • James Stewart, Daniel K. Clegg, Saleem Watson, Calculus: Early Transcendentals, 9th edition, Cengage, 2021.
  • Joel Hass, Christopher Heil, Przemyslaw Bogacki, Maurice D. Weir, George B. Thomas Jr., Thomas' Calculus, 15th edition, Pearson, 2023.
  • Jerrold E. Marsden, Anthony Tromba, Vector Calculus, 6th edition, W. H. Freeman, 2012.
  • Stephen Boyd, Lieven Vandenberghe, Convex Optimization, 1st edition, Cambridge University Press, 2004.
  • Jorge Nocedal, Stephen J. Wright, Numerical Optimization, 2nd edition, Springer, 2006.
  • Gilbert Strang, Introduction to Linear Algebra, 6th edition, Wellesley-Cambridge Press, 2023.
  • David C. Lay, Steven R. Lay, Judi J. McDonald, Linear Algebra and Its Applications, 6th edition, Pearson, 2021.