For further reading there is an excellent resource here http://web.mit.edu/15.053/www/ http://web.mit.edu/15.053/www/ with a leitmotive example and both theoret

For further reading there is an excellent resource here http://web.mit.edu/15.053/www/ http://web.mit.edu/15.053/www/ with a leitmotive example and both theoretical and practical approches. It also explains in detail the theory behind the real world implementation of simplex method (aka revised simplex) with inversed matrix factorization methods, etc. This is a quite old book but as far as I know even modern linear solvers use those techniques (at least open-source ones for sure). This is a very specific and little subset of convex optimization and yet very powerful (and exciting). Convex optimization methods can also be used in many non-convex situations, for example when you have quasi-convex or log-convex functions as objective or constraints. I recommend Boyd's excellent course https://lagunita.stanford.edu/courses/Engineering/CVX101/Winter2014/about https://lagunita.stanford.edu/courses/Engineering/CVX101/Win... and the online free book https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf (warning : very addictive)