2016 INFORMS Annual Meeting Program

459 / 561

INFORMS Nashville – 2016
459
2 - Effects Of Disjunctive Conic Cuts Within A Branch And Conic
Cut Algorithm
Sertalp Bilal Cay, Lehigh University,
Recently, Mixed Integer Second Order Cone Optimization (MISOCO) has gained
attention. This interest has been driven by the availability of efficient methods to
solve second order cone optimization (SOCO) problems and the wide range of
applications of MISOCO. In this work, we experiment with the recently
developed methodology of Disjunctive Conic Cuts (DCC). In particular, we test it
on variations of portfolio optimization problems within a Branch and Conic Cut
(BCC) framework. Our experiments show that our proposed methodology using
DCCs is effective in practical settings.
3 - A Rounding Procedure For A Maximally Complementary Solution
Of Second Order Conic Optimization
Ali Mohammad Nezhad, Lehigh,
The concept of optimal partition was originally introduced for linear optimization,
and later the concept was extended to second-order conic optimization. In this
paper, we present a rounding procedure, which uses optimal partition
information to generate a pair of maximally complementary optimal solutions.
The rounding procedure starts from a strictly interior solution, close to the
optimal set. It either gives an exact maximally complementary optimal solution in
strongly polynomial time, or provides a fast iterative procedure to approximate a
maximally complementary optimal solution.
WD12
104B-MCC
Recent Advances in Discrete Optimization
Sponsored: Optimization, Integer and Discrete Optimization
Sponsored Session
Chair: Andres Gomez Escobar, University of California, Berkeley, CA,
United States,
1 - Final Point Generalized Intersection Cuts
Aleksandr Mark Kazachkov, Carnegie Mellon University,
We introduce a new class of cuts for mixed-integer programming called final
point cuts. Generated from arbitrary valid disjunctions, this class is an extension
of the generalized intersection cut paradigm. We present theoretical and
computational results demonstrating the strength of these cuts as compared to
both standard intersection cuts and previous types of generalized intersection
cuts.
2 - Generating Cuts From The Ramping Polytope For The Unit
Commitment Problem
Jim Ostrowski, University of Tennessee,
We present a perfect formulation for a single generator in the unit commitment
(UC) problem. This generator can have characteristics such as ramping
constraints, time-dependent start-up costs, and start-up/shut-down ramping. The
perfect formulation is polynomially large, so we use it to create a cut-generating
linear program for a single generator. We test the computational efficacy of these
cuts in a utility-scale UC MIP model, based on the FERC generator set and 2015
hourly data from PJM. These cuts may also beneficial for the more complex UC
models used by ISOs today because they reduce the enumeration needed to
enforce a generator’s technical constraints.
3 - Conic Quadratic Program With Indicator Variables
Hyemin Jeon, University of California Berkeley,
Alper Atamturk
We investigate a conic quadratic mixed 0-1 programming problem with on-off
constraints that arises from mean risk optimization. Analysis is conducted on the
properties of the optimal solutions of the problem and its relaxation. A class of
linear valid inequalities is derived by lifting the extended polymatroid inequalities
that are known to be highly effective for the pure 0-1 case. We report the result
of computational experiments to demonstrate the impact of the inequalities on
strengthening the continuous relaxation.
4 - Polymatroid Inequalities For Mixed-integer Second Order
Cone Programming
Andres Gomez, University of California Berkeley,
, Alper Atamturk
We propose valid inequalities for mixed-integer second order cones programs. We
show that the proposed inequalities completely describe the convex hull of a
single second order cone constraint. Moreover we show how to strengthen the
inequalities using other constraints of the optimization problem. We report
computational experiments on mixed-integer second-order cone programs, using
the proposed valid inequalities as cutting planes.
WD13
104C-MCC
Computational Optimization and Software
Sponsored: Optimization, Computational Optimization and Software
Sponsored Session
Chair: Robert J Vanderbei, Princeton University, Princeton, NJ, 08544,
United States,
Co-Chair: Hande Benson, Drexel University, LeBow College of
Business, Philadelphia, PA, 19104, United States,
1 - Cubic Regularization For Conjugate Gradient Minimization And
Symmetric Rank-one Methods
Hande Benson, Drexel University,
Regularization techniques have been used to help existing algorithms solve
“difficult” nonlinear optimization problems. Over the last decade, regularization
has been proposed to remedy issues with equality constraints and equilibrium
constraints, bound Lagrange multipliers, and identify infeasible problems. In this
talk, we will focus on the application of cubic regularization in the context of the
symmetric rank-one and conjugate gradient methods for nonlinear programming.
2 - Two New Efficient Algorithms For Compressed Sensing
Robert J Vanderbei, Princeton University,
We present two new approaches for solving large-scale compressed sensing
problems. The first approach uses the parametric simplex method to recover very
sparse signals by taking a small number of simplex pivots while the second
approach reformulates the problem using Kronecker products to achieve faster
computation via a sparser problem formulation. Numerical studies show that each
of these two algorithms are about 10 times faster than current state-of-the-art
methods.
3 - Using A Fast Projected Gradient Method To Solve Large Scale
Nonlinear Optimization Problems.
Igor Griva, George Mason University,
Some modern nonlinear optimization problems have a large number of variables
and dense Hessians that makes challenging or impossible using second order
methods. We discuss how the fast projected gradient method can be used for
addressing optimization problems in machine learning, nonnegative least squares
and positive emission tomography reconstruction.
WD14
104D-MCC
Facility Location I
Contributed Session
Chair: David Mendez, Graduate Student, University of Tennessee, 2621
Morgan Circle, Knoxville, TN, 37996, United States,
1 - The Price Of Deception In Social Choice
James Patrick Bailey, Georgia Institute of Technology, 755 Ferst
Drive, NW, Unit 2401, Atlanta, GA, 30332, United States,
Craig Aaron Tovey
Classic impossibility theorems rule out strategy-proofness for all popular voting
rules, but is it wise to always settle for nothing with respect to manipulation? We
introduce a measure, the price of deception, of how much strategic voting could
alter an election outcome with respect to the winning criterion. We analyze this
measure for several standard voting rules and find significant differences among
them. We also analyze several standard cost functions for facility location, and
again find large differences. These results give good evidence that the price of
deception, like computational complexity, does offer finer distinctions of
manipulability than between ”yes” or ”no”.
2 - A Comprehensive Model For Location/allocation Of Maritime
Search And Rescue Vessels
Amin Akbari, PhD Candidate, Dalhousie University, 1104-1094
Wellington Street, Halifax, NS, B3H 2Z9, Canada,
Ronald P Pelot, H A Eiselt
The general methodology utilized in this study is building a mathematical model
to optimize the Location/Allocation of Maritime Search and Rescue Resources
with regard to several criteria such as primary and backup coverage, mean access
time and service equality. A multi-objective model is built to optimize the location
and response allocation of SAR resources with simultaneously taking several
objectives into account in order to achieve greater responsiveness and resource
utilization. Atlantic Canada serves as the area of the study. Sensitivity analyses are
performed on variable objective weights and other parameters to examine their
impact on the optimal solution.
WD14