The auction algorithm : a distributed relaxation method for the assignment problem

Bibliography: p. 15-19.Work supported by grant NSF-ECS-8217668by Dimitri P. Bertsekas

Auction algorithms for network flow problems : a tutorial introduction

Caption title.Includes bibliographical references (p. 13-15).Research supported by the National Science Foundation. DDM-8903385 CCR-9103804 Research supported by the Army Research Office. DAAL03-86-K-0171by Dimitri P. Bertsekas

AC OPF in Radial Distribution Networks - Parts I,II

The optimal power-flow problem (OPF) has played a key role in the planning and operation of power systems. Due to the non-linear nature of the AC power-flow equations, the OPF problem is known to be non-convex, therefore hard to solve. Most proposed methods for solving the OPF rely on approximations that render the problem convex, but that may yield inexact solutions. Recently, Farivar and Low proposed a method that is claimed to be exact for radial distribution systems, despite no apparent approximations. In our work, we show that it is, in fact, not exact. On one hand, there is a misinterpretation of the physical network model related to the ampacity constraint of the lines' current flows. On the other hand, the proof of the exactness of the proposed relaxation requires unrealistic assumptions related to the unboundedness of specific control variables. We also show that the extension of this approach to account for exact line models might provide physically infeasible solutions. Recently, several contributions have proposed OPF algorithms that rely on the use of the alternating-direction method of multipliers (ADMM). However, as we show in this work, there are cases for which the ADMM-based solution of the non-relaxed OPF problem fails to converge. To overcome the aforementioned limitations, we propose an algorithm for the solution of a non-approximated, non-convex OPF problem in radial distribution systems that is based on the method of multipliers, and on a primal decomposition of the OPF. This work is divided in two parts. In Part I, we specifically discuss the limitations of BFM and ADMM to solve the OPF problem. In Part II, we provide a centralized version and a distributed asynchronous version of the proposed OPF algorithm and we evaluate its performances using both small-scale electrical networks, as well as a modified IEEE 13-node test feeder

The auction algorithm for assignment and other network flow problems

Cover title.Includes bibliographical references (p. 15-17).Research supported by the Army Research Office. DAAL 03-86-K-0171by Dimitri P. Bertsekas

Parallel primal-dual methods for the minimum cost flow problem

"This report is a substantial revision of report LIDS-P-1998, September 1990."Includes bibliographical references (p. 20-21).Supported by the BM/C3 Technology branch of the U.S. Army Strategic Defense Command.by Dimitri P. Bertsekas and David A. Castañon

Auction algorithms for network flow problems : a tutorial introduction

"May 1992." "This is a greatly revised version of the earlier report LIDS-P-2064."Includes bibliographical references (p. 47-50).Supported by NSF. DDM-8903385 CCR-9103804 Supported by the ARO. DAAL03-86-K-0171by Dimitri P. Bertsekas

Parallel asynchronous primal-dual methods for the minimum cost flow problem

Cover title. "September 1990."Includes bibliographical references (p. 18-19).Research supported by the BM/C3 Technology branch of the United States Army Strategic Defense Command.by Dimitri P. Bertsekas and David A. Castañon

Survey on Combinatorial Register Allocation and Instruction Scheduling

Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization