These are the presentations that were made by the IBM Decision Optimization team at INFORMS, Nashville, November 2016.

Xavier Nodet, Andrea Tramontani, IBM Workshop
In this presentation, we discuss where Decision Optimization fits in the IBM ecosystem. We also present the new features available in CPLEX and CP Optimizer.

2016-11-12 – INFORMS IBM Workshop.pdf

Andrea Lodi, Solving Standard Quadratic Programming By Cutting Planes
Standard quadratic programs are non-convex quadratic programs with the only constraint that variables must belong to a simplex. By a famous result of Motzkin and Straus, those problems are connected to the clique number of a graph. We propose cutting planes to obtain strong bounds: our cuts are derived in the context of Spatial Branch & Bound, where linearization variables represent products. Their validity is based on Motzkin-Straus result. We study the relation between these cuts and the ones obtained by the first RLT level. We present extensive computational results using the cuts in the context of the Spatial Branch & Bound implemented by the commercial solver CPLEX.

2016-11-13 – QP Cutting Planes.pdf

Xavier Nodet, What’s new in CPLEX Optimization Studio 12.7?
This presentation covers some of the new features available in CPLEX Optimization Studio 12.7. We focus in particular on Modeling Assistance.

2016-11-14 – COS 12.7.pdf

Ed Klotz, Improved Analysis Of Infeasible Mixed-integer Linear And Quadratic Programs
Analysis of infeasible MILPs and MIQPs is complicated by the integer restrictions (IRs). Current techniques return a minimal infeasible subset of the linear constraints and variable bounds by solving a series of MILPs. They do not find a minimal subset of the IRs, because of the significant additional computational cost. We develop efficient ways to find a minimal subset of the IRs and use this to speed the isolation of a true Irreducible Infeasible Subset for MILPs and MIQPs. This is helpful when a variable is accidentally specified as integer.

2016-11-15 – Infeasibility Analysis.pdf

Ed Klotz, Performance Tuning For CPLEX’s Spatial Branch-and-bound Solver For Global Nonconvex Mixed Integer Quadratic Programs
MILP solvers have been improving for more than 40 years, and performance tuning tactics regarding both adjusting solver strategies and model formulations have evolved as well. State-of-the-art global nonconvex MIQP solvers have improved dramatically in recent years, but they lack the benefit of 40 years of evolution. Also, they use a broader notion of branching that can create different performance challenges. This talk will assess the effectiveness of existing MILP tuning tactics for solving nonconvex MIQPs, as well as consider more specific strategies for this challenging type of problem.

2016-11-15 – SpatialPerfTuning.pdf

Andrea Tramontani, Recent Advances in IBM CPLEX Optimizer
This presentation covers the latest developments in the CPLEX Optimizer. We present some of the new features and algorithmic techniques recently added to CPLEX (we focus in particular on Benders’ decomposition algorithm) and we give benchmark results to assess the performance improvements achieved in the latest CPLEX version.

2016-11-16 – What’s new in CPLEX 12.7.pdf