14 research outputs found
Existence of Local Saddle Points for a New Augmented Lagrangian Function
We give a new class of augmented Lagrangian functions for nonlinear
programming problem with both equality and inequality constraints. The close relationship
between local saddle points of this new augmented Lagrangian and local optimal
solutions is discussed. In particular, we show that a local saddle point is a local optimal
solution and the converse is also true under rather mild conditions
New Convergence Properties of the Primal Augmented Lagrangian Method
New convergence properties of the proximal augmented Lagrangian method is established for continuous nonconvex optimization problem with both equality and inequality constrains. In particular, the multiplier sequences are not required to be bounded. Different convergence results are discussed dependent on whether the iterative sequence {xk} generated by algorithm is convergent or divergent. Furthermore, under certain convexity assumption, we show that every accumulation point of {xk} is either a degenerate point or a KKT point of the primal problem. Numerical experiments are presented finally
Decomposition Methods for Global Solutions of Mixed-Integer Linear Programs
This paper introduces two decomposition-based methods for two-block
mixed-integer linear programs (MILPs), which break the original problem into a
sequence of smaller MILP subproblems. The first method is based on the
l1-augmented Lagrangian. The second method is based on the alternating
direction method of multipliers. When the original problem has a block-angular
structure, the subproblems of the first block have low dimensions and can be
solved in parallel. We add reverse-norm cuts and augmented Lagrangian cuts to
the subproblems of the second block. For both methods, we show asymptotic
convergence to globally optimal solutions and present iteration upper bounds.
Numerical comparisons with recent decomposition methods demonstrate the
exactness and efficiency of our proposed methods