LP approximations to mixed-integer polynomial optimization problems

We present a class of linear programming approximations for constrained optimization problems. In the case of mixed-integer polynomial optimization problems, if the intersection graph of the constraints has bounded tree-width our construction yields a class of linear size formulations that attain any desired tolerance. As a result, we obtain an approximation scheme for the "AC-OPF" problem on graphs with bounded tree-width. We also describe a more general construction for pure binary optimization problems where individual constraints are available through a membership oracle; if the intersection graph for the constraints has bounded tree-width our construction is of linear size and exact. This improves on a number of results in the literature, both from the perspective of formulation size and generality.

[1]  Jesse T. Holzer,et al.  Implementation of a Large-Scale Optimal Power Flow Solver Based on Semidefinite Programming , 2013, IEEE Transactions on Power Systems.

[2]  Daniel Bienstock,et al.  Histogram Models for Robust Portfolio Optimization , 2007 .

[3]  Dimitris Gatzouras,et al.  Lower Bound for the Maximal Number of Facets of a 0/1 Polytope , 2005, Discret. Comput. Geom..

[4]  S. Low,et al.  Zero Duality Gap in Optimal Power Flow Problem , 2012, IEEE Transactions on Power Systems.

[5]  Daniel Bienstock,et al.  Tree-width and the Sherali-Adams operator , 2004, Discret. Optim..

[6]  Venkat Chandrasekaran,et al.  Complexity of Inference in Graphical Models , 2008, UAI.

[7]  V. Kaibel Extended Formulations in Combinatorial Optimization , 2011, 1104.1023.

[8]  Eugene L. Lawler,et al.  Linear-Time Computation of Optimal Subgraphs of Decomposable Graphs , 1987, J. Algorithms.

[9]  K. Mani Chandy,et al.  Quadratically Constrained Quadratic Programs on Acyclic Graphs With Application to Power Flow , 2012, IEEE Transactions on Control of Network Systems.

[10]  Hanif D. Sherali,et al.  New reformulation linearization/convexification relaxations for univariate and multivariate polynomial programming problems , 1997, Oper. Res. Lett..

[11]  Attila Pór,et al.  On 0-1 Polytopes with Many Facets , 2001 .

[12]  Jean B. Lasserre,et al.  A bounded degree SOS hierarchy for polynomial optimization , 2015, EURO J. Comput. Optim..

[13]  Daniel Bienstock,et al.  Subset Algebra Lift Operators for 0-1 Integer Programming , 2004, SIAM J. Optim..

[14]  Carleton Coffrin,et al.  NESTA, The NICTA Energy System Test Case Archive , 2014, ArXiv.

[15]  Derek G. Corneil,et al.  Complexity of finding embeddings in a k -tree , 1987 .

[16]  DechterRina,et al.  Tree clustering for constraint networks (research note) , 1989 .

[17]  D. Bienstock,et al.  Chapter 8 Algorithmic implications of the graph minor theorem , 1995 .

[18]  Martin W. P. Savelsbergh,et al.  Combining Exact and Heuristic Approaches for the Capacitated Fixed-Charge Network Flow Problem , 2010, INFORMS J. Comput..

[19]  Masakazu Kojima,et al.  Sparsity in sums of squares of polynomials , 2005, Math. Program..

[20]  John E. Mitchell,et al.  Interior Point Methods for Large-Scale Linear Programming , 2006, Handbook of Optimization in Telecommunications.

[21]  Hanif D. Sherali,et al.  A Hierarchy of Relaxations Between the Continuous and Convex Hull Representations for Zero-One Programming Problems , 1990, SIAM J. Discret. Math..

[22]  Andrea Lodi,et al.  On the MIR Closure of Polyhedra , 2007, IPCO.

[23]  Monique Laurent,et al.  A Comparison of the Sherali-Adams, Lovász-Schrijver, and Lasserre Relaxations for 0-1 Programming , 2003, Math. Oper. Res..

[24]  Michael I. Jordan,et al.  Treewidth-based conditions for exactness of the Sherali-Adams and Lasserre relaxations , 2004 .

[25]  Paul D. Seymour,et al.  Graph minors. III. Planar tree-width , 1984, J. Comb. Theory B.

[26]  Myun-Seok Cheon,et al.  Solving Mixed Integer Bilinear Problems Using MILP Formulations , 2013, SIAM J. Optim..

[27]  Abhinav Verma,et al.  Power grid security analysis: an optimization approach , 2010 .

[28]  Masakazu Muramatsu,et al.  Sums of Squares and Semidefinite Programming Relaxations for Polynomial Optimization Problems with Structured Sparsity , 2004 .

[29]  Daniel K. Molzahn,et al.  Application of semidefinite optimization techniques to problems in electric power systems , 2013 .

[30]  Levent Tunçel,et al.  A Comprehensive Analysis of Polyhedral Lift-and-Project Methods , 2016, SIAM J. Discret. Math..

[31]  B. Gluss AN INTRODUCTION TO DYNAMIC PROGRAMMING , 1961 .

[32]  Warren P. Adams,et al.  A hierarchy of relaxation between the continuous and convex hull representations , 1990 .

[33]  D. R. Fulkerson,et al.  Incidence matrices and interval graphs , 1965 .

[34]  M. Laurent Sums of Squares, Moment Matrices and Optimization Over Polynomials , 2009 .

[35]  ArnborgStefan Efficient algorithms for combinatorial problems on graphs with bounded, decomposabilitya survey , 1985 .

[36]  R. Halin S-functions for graphs , 1976 .

[37]  Hans L. Bodlaender,et al.  Dynamic Programming on Graphs with Bounded Treewidth , 1988, ICALP.

[38]  Stefan Arnborg,et al.  Efficient algorithms for combinatorial problems on graphs with bounded decomposability — A survey , 1985, BIT.

[39]  Bruce M. Bennett,et al.  INTRODUCTION TO DYNAMICS , 1989 .

[40]  Judea Pearl,et al.  Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach , 1982, AAAI.

[41]  Günter M. Ziegler,et al.  Extremal Properties of 0/1-Polytopes , 1997, Discret. Comput. Geom..

[42]  William H. Cunningham,et al.  On Integer Programming and the Branch-Width of the Constraint Matrix , 2007, IPCO.

[43]  Thorsten Koch,et al.  Evaluating Gas Network Capacities , 2015, MOS-SIAM Series on Optimization.

[44]  Paul D. Seymour,et al.  Graph Minors: XV. Giant Steps , 1996, J. Comb. Theory, Ser. B.

[45]  Egon Balas,et al.  A lift-and-project cutting plane algorithm for mixed 0–1 programs , 1993, Math. Program..

[46]  Michael R. Fellows,et al.  Polynomial-time self-reducibility: theoretical motivations and practical results ∗ , 1989 .

[47]  Jean B. Lasserre,et al.  Convergent SDP-Relaxations for Polynomial Optimization with Sparsity , 2006, ICMS.

[48]  Jean B. Lasserre,et al.  An Explicit Exact SDP Relaxation for Nonlinear 0-1 Programs , 2001, IPCO.

[49]  Jiawang Nie,et al.  Optimality conditions and finite convergence of Lasserre’s hierarchy , 2012, Math. Program..

[50]  Michael I. Jordan Graphical Models , 2003 .

[51]  Eugene C. Freuder A sufficient condition for backtrack-bounded search , 1985, JACM.

[52]  Alexander Schrijver,et al.  Cones of Matrices and Set-Functions and 0-1 Optimization , 1991, SIAM J. Optim..

[53]  Michael I. Jordan,et al.  Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..

[54]  Rutherford Aris,et al.  Optimization of multistage cyclic and braching systems by serial procedures , 1964 .

[55]  Javad Lavaei,et al.  Exactness of Semidefinite Relaxations for Nonlinear Optimization Problems with Underlying Graph Structure , 2014, SIAM J. Optim..

[56]  F. Glover IMPROVED LINEAR INTEGER PROGRAMMING FORMULATIONS OF NONLINEAR INTEGER PROBLEMS , 1975 .

[57]  J. Hooker,et al.  Logic-Based Methods for Optimization: Combining Optimization and Constraint Satisfaction , 2000 .

[58]  Hans L. Bodlaender A linear time algorithm for finding tree-decompositions of small treewidth , 1993, STOC '93.

[59]  Umberto Bertelè,et al.  Nonserial Dynamic Programming , 1972 .

[60]  David Grimm,et al.  A note on the representation of positive polynomials with structured sparsity , 2006, math/0611498.

[61]  Paul D. Seymour,et al.  Graph Minors. II. Algorithmic Aspects of Tree-Width , 1986, J. Algorithms.