3,405 research outputs found
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Optimization methods are at the core of many problems in signal/image
processing, computer vision, and machine learning. For a long time, it has been
recognized that looking at the dual of an optimization problem may drastically
simplify its solution. Deriving efficient strategies which jointly brings into
play the primal and the dual problems is however a more recent idea which has
generated many important new contributions in the last years. These novel
developments are grounded on recent advances in convex analysis, discrete
optimization, parallel processing, and non-smooth optimization with emphasis on
sparsity issues. In this paper, we aim at presenting the principles of
primal-dual approaches, while giving an overview of numerical methods which
have been proposed in different contexts. We show the benefits which can be
drawn from primal-dual algorithms both for solving large-scale convex
optimization problems and discrete ones, and we provide various application
examples to illustrate their usefulness
Projection Methods: Swiss Army Knives for Solving Feasibility and Best Approximation Problems with Halfspaces
We model a problem motivated by road design as a feasibility problem.
Projections onto the constraint sets are obtained, and projection methods for
solving the feasibility problem are studied. We present results of numerical
experiments which demonstrate the efficacy of projection methods even for
challenging nonconvex problems
On feasibility based bounds tightening
http://www.optimization-online.org/DB_HTML/2012/01/3325.htmlMathematical programming problems involving nonconvexities are usually solved to optimality using a (spatial) Branch-and-Bound algorithm. Algorithmic e?ciency depends on many factors, among which the widths of the bounding box for the problem variables at each Branch-and-Bound node naturally plays a critical role. The practically fastest box-tightening algorithm is known as FBBT (Feasibility-Based Bounds Tightening): an iterative procedure to tighten the variable ranges. Depending on the instance, FBBT may not converge ?nitely to its limit ranges, even in the case of linear constraints. Tolerance-based termination criteria yield ?nite termination, but not in worstcase polynomial-time. We model FBBT by using ?xed-point equations in terms of the variable bounding box, and we treat these equations as constraints of an auxiliary mathematical program. We demonstrate that the auxiliary mathematical problem is a linear program, which can of course be solved in polynomial time. We demonstrate the usefulness of our approach by improving an existing Branch-and-Bound implementation. global optimization, MINLP, spatial Branch-and-Bound, range reduction
Optimal stability polynomials for numerical integration of initial value problems
We consider the problem of finding optimally stable polynomial approximations
to the exponential for application to one-step integration of initial value
ordinary and partial differential equations. The objective is to find the
largest stable step size and corresponding method for a given problem when the
spectrum of the initial value problem is known. The problem is expressed in
terms of a general least deviation feasibility problem. Its solution is
obtained by a new fast, accurate, and robust algorithm based on convex
optimization techniques. Global convergence of the algorithm is proven in the
case that the order of approximation is one and in the case that the spectrum
encloses a starlike region. Examples demonstrate the effectiveness of the
proposed algorithm even when these conditions are not satisfied
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