David G. Luenberger

TL;DR: Strodiot and Zentralblatt as discussed by the authors introduced the concept of unconstrained optimization, which is a generalization of linear programming, and showed that it is possible to obtain convergence properties for both standard and accelerated steepest descent methods.

TL;DR: Strodiot and Zentralblatt as mentioned in this paper introduced the concept of unconstrained optimization, which is a generalization of linear programming, and showed that it is possible to obtain convergence properties for both standard and accelerated steepest descent methods.

TL;DR: Although CLP has long been known to be convex optimization problems, no efficient solution algorithm was known until about two decades ago, when it was discovered that interior-point algorithms for LP can be adapted to solve certain CLPs with both theoretical and practical efficiency.

TL;DR: A linear program (LP) is an optimization problem in which the objective function is linear in the unknowns and the constraints consist of linear equalities and linear inequalities as discussed by the authors, and the exact form of these constraints may differ from one problem to another, but as shown below, any linear program can be transformed into the following standard form:

TL;DR: For a problem with n variables and m constraints, penalty and barrier methods work directly in the n-dimensional space of variables, as compared to primal methods that work in (n − m)-dimensional space.