A convex nonsmooth optimization problem is replaced by a sequence of line search problems along recursively updated rays. Convergence of the method is proved and applications to linear inequalities, constraint aggregation and saddle point seeking indicated.

A general constraint aggregation technique is proposed for convex optimization problems. At each iteration a set of convex inequalities and linear equations is replaced by a single inequality formed as a linear combination of the original constraints. After solving the simplified subproblem, new...

A general class of iterative methods for saddle point seeking is developed. The directions used are subgradients evaluated at perturbed points. Convergence of the methods is proved and alternative strategies for implementation are discussed. The procedure suggests scalable algorithms for solving...

The linear programming problem is shown to be equivalent to a game in which primal players minimize the augmented Lagrangian function for the primal problem and dual players maximize the augmented Lagrangian function for the dual problem. Based on that, a parallel solution method is developed in...

This article generalizes the Nash equilibrium approach to linear programming to the saddle point problem. The problem is shown to be equivalent to a non-zero sum game in which objectives of the players are obtained by partial regularization of the original function. Based on that, a solution...

A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into scenarios or decomposing it into nodes...

A class of non-cooperative constrained games is analyzed for which the Ky Fan function is convex-concave. Nash equilibria of such games correspond to diagonal saddle points of the said function. This feature is exploited in designing computational algorithms for finding such equilibria.