147,325 research outputs found
Mathematical Programming Decoding of Binary Linear Codes: Theory and Algorithms
Mathematical programming is a branch of applied mathematics and has recently
been used to derive new decoding approaches, challenging established but often
heuristic algorithms based on iterative message passing. Concepts from
mathematical programming used in the context of decoding include linear,
integer, and nonlinear programming, network flows, notions of duality as well
as matroid and polyhedral theory. This survey article reviews and categorizes
decoding methods based on mathematical programming approaches for binary linear
codes over binary-input memoryless symmetric channels.Comment: 17 pages, submitted to the IEEE Transactions on Information Theory.
Published July 201
Loosely Coupled Formulations for Automated Planning: An Integer Programming Perspective
We represent planning as a set of loosely coupled network flow problems,
where each network corresponds to one of the state variables in the planning
domain. The network nodes correspond to the state variable values and the
network arcs correspond to the value transitions. The planning problem is to
find a path (a sequence of actions) in each network such that, when merged,
they constitute a feasible plan. In this paper we present a number of integer
programming formulations that model these loosely coupled networks with varying
degrees of flexibility. Since merging may introduce exponentially many ordering
constraints we implement a so-called branch-and-cut algorithm, in which these
constraints are dynamically generated and added to the formulation when needed.
Our results are very promising, they improve upon previous planning as integer
programming approaches and lay the foundation for integer programming
approaches for cost optimal planning
Engineering Optimization: Methods/Applications - Colorado State University
This course provides a comprehensive treatment of methods of optimization with focus on linear programming and its extensions, network flow optimization, integer programming, quadratic programming, and an introduction to nonlinear programming. The goal is to maintain a balance between theory, numerical computation, problem setup for solution by computer algorithms, and engineering applications. Course taught at Colorado State University
Model of Optimum Placement of Servers and Web-Contents in Content Delivery Systems
A new model of optimum placement of servers and Web contents in a Content Delivery Network that is intended to minimize the cost of delivery of content to the ultimate users is proposed. The
model also takes into account the structure of the network and the weight of each Web content in the
network nodes. A mathematical formulation of the proposed model reduces to a problem of linear integer programming. In the present study synthesis of a neural network for the solution of a problem of
linear integer programming is also described
Solving Lotsizing Problems on Parallel Identical Machines Using Symmetry Breaking Constraints
Production planning on multiple parallel machines is an interesting problem, both from a theoretical and practical point of view. The parallel machine lotsizing problem consists of finding the optimal timing and level of production and the best allocation of products to machines. In this paper we look at how to incorporate parallel machines in a Mixed Integer Programming model when using commercial optimization software. More specifically, we look at the issue of symmetry. When multiple identical machines are available, many alternative optimal solutions can be created by renumbering the machines. These alternative solutions lead to difficulties in the branch-and-bound algorithm. We propose new constraints to break this symmetry. We tested our approach on the parallel machine lotsizing problem with setup costs and times, using a network reformulation for this problem. Computational tests indicate that several of the proposed symmetry breaking constraints substantially improve the solution time, except when used for solving the very easy problems. The results highlight the importance of creative modeling in solving Mixed Integer Programming problems.Mixed Integer Programming;Formulations;Symmetry;Lotsizing
Optimal Economic Schedule for a Network of Microgrids With Hybrid Energy Storage System Using Distributed Model Predictive Control
Artículo Open Access en el sitio web el editor. Pago por publicar en abierto.In this paper, an optimal procedure for the economic schedule of a network of interconnected microgrids with hybrid energy storage system is carried out through a control algorithm based on distributed model predictive control (DMPC). The algorithm is specifically designed according to the criterion of improving the cost function of each microgrid acting as a single system through the network mode operation. The algorithm allows maximum economical benefit of the microgrids, minimizing the degradation causes of each storage system, and fulfilling the different system constraints. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled with the framework of mixed logic dynamic. The DMPC problem is solved with the use of mixed integer linear programming using a piecewise formulation, in order to linearize a mixed integer quadratic programming problem.Ministerio de Economía, Industria y Competitivadad DPI2016-78338-RComisión Europea 0076-AGERAR-6-
Simulation of Cellular Network Model by Integer Programming
Integer programming is a particular form or variety of the linear program, in which one or more of its values in the solution vector have integer. Integer programming can be applied on the network analysis and telecommunication. In this paper, integer programming is used to solve the problems of optimizing the route between cell i and HUB (Home Unit Base) j so that the cost for making a network model, especially cellular network, can be minimized
An Integer Linear Programming Solution to the Telescope Network Scheduling Problem
Telescope networks are gaining traction due to their promise of higher
resource utilization than single telescopes and as enablers of novel
astronomical observation modes. However, as telescope network sizes increase,
the possibility of scheduling them completely or even semi-manually disappears.
In an earlier paper, a step towards software telescope scheduling was made with
the specification of the Reservation formalism, through the use of which
astronomers can express their complex observation needs and preferences. In
this paper we build on that work. We present a solution to the discretized
version of the problem of scheduling a telescope network. We derive a solvable
integer linear programming (ILP) model based on the Reservation formalism. We
show computational results verifying its correctness, and confirm that our
Gurobi-based implementation can address problems of realistic size. Finally, we
extend the ILP model to also handle the novel observation requests that can be
specified using the more advanced Compound Reservation formalism.Comment: Accepted for publication in the refereed conference proceedings of
the International Conference on Operations Research and Enterprise Systems
(ICORES 2015
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