Welcome to the Webinar. What s New in Gurobi 7.5

Transcription

1 Welcome to the Webinar What s New in Gurobi 7.5

2 Speaker Introduction Dr. Tobias Achterberg Director of R&D at Gurobi Optimization Formerly a developer at ILOG, where he worked on CPLEX 11.0 to 12.6 Obtained his degree in mathematics and computer science from the Technical University of Berlin and the Zuse Institute Berlin, then finished doctorate in mathematics with Prof. Martin Grötschel in 2007 Dr. Achterberg is the author of SCIP which is regarded as the best academic MIP solver 2

3 What's New? New features in Gurobi 7.5 Improved Python interface Multi-objective improvements JavaDoc documentation Support new versions of Python, R, Visual Studio Compute Server communication statistics New parameters Additional minor enhancements Performance improvements 3

4 Python Modeling Enhancements 4

5 Python Modeling Enhancements Significant simplification of our Python API Simpler implementation of mathematical models Model.addConstr: handles general constraints, range constraints and indicator constraints Model.addConstrs: allows you to use Python generator expressions over all constraint types GCHelper: helper functions for handling general constraints Python models become more concise and easier to read 5

6 Python Modeling Enhancements addconstr() Integrated algebraic interface for adding constraints Old: model.addqconstr( x*x + y*y <= 2.0, c1 ) model.addrange( x+y, 1.0, 2.0, c2 ) model.addgenconstrindicator( x, True, y+z == 1.0, c3 ) model.addgenconstrmax( x, [y,z], 2.0, c4 ) New: model.addconstr( x*y + y*y <= 2.0, c1 ) model.addconstr( x+y == [1.0,2.0], c2 ) model.addconstr((x == 1) >> (y+z == 1.0), c3 ) model.addconstr( x == max_(y,z,2.0), c4 ) 6

7 Python Modeling Enhancements Helper Functions General constraint helper functions: We introduced functions max_(), min_(), abs_(), and_(), or_() to simplify construction of general constraints model.addconstr(x == abs_(y)) model.addconstr(x == or_(y,z,w)) Note: any_() can be used as a synonym for or_() Integrate general constraints with addconstrs() The new syntax allows you to add sets of general constraints in batches Old: X, Y, Gcons = model.addvars(10), model.addvars(10), {} for i in X: Gcons[i] = model.addgenconstrmin(x[i], [Y[i], 10], name= Gc ) New: X, Y = model.addvars(10), model.addvars(10) Gcons = model.addconstrs((x[i] == min_(y[i],10) for i in X), name= Gc ) 7

8 Python Modeling Enhancements remove() We extended model.remove() to handle lists, tuples and dictionaries of objects Old: for i in model.getconstrs(): model.remove(i) New: model.remove(model.getconstrs()) 8

9 Enhanced Multi-Objective API 9

10 Enhanced Multi-Objective API Easier definition of multiple objectives We introduced model.setobjectiven(linexpr, index,...) Old (Gurobi 7.0): coef1 = [0, 1, 2, 3, 4] coef2 = [4, 3, 2, 1, 0] x = model.addvars(5) model.numobj = 2 model.params.objnumber = 0 model.objnpriority = 2 model.objnweight = 1 model.setattr(grb.attr.objn, x, coef1) model.params.objnumber = 1 model.objnpriority = 1 model.objnweight = 1 model.setattr(grb.attr.objn, x, coef2) New (Gurobi 7.5): coef1 = [0, 1, 2, 3, 4] coef2 = [4, 3, 2, 1, 0] x = model.addvars(5) model.setobjectiven(x.prod(coef1), 0, priority=2, weight=1) model.setobjectiven(x.prod(coef2), 1, priority=1, weight=1) Or equivalently: x = model.addvars(5) model.setobjectiven(x[1]+2*x[2]+3*x[3]+4*x[4], 0, priority=2, weight=1) model.setobjectiven(4*x[0]+3*x[1]+2*x[2]+x[3], 1, priority=1, weight=1) 10

11 Enhanced Multi-Objective API Enhanced termination control for multi-objective optimization We introduced model.getmultiobjenv(): env0 = model.getmultiobjenv(0) env1 = model.getmultiobjenv(1) env0.setparam( TimeLimit,100) env1.setparam( TimeLimit,10) model.optimize() model.discardmultiobjenvs() Allows for fine-grained control of each multi-objective optimization pass Algorithmic choices Termination criteria One optimization pass per objective priority level Settings for additional objectives of same priority level are ignored Use MIP starts for first priority level objective solve 11

12 Other Enhancements 12

13 JavaDoc Documentation Contains our full documentation Can be used in any IDE that supports JavaDoc, e.g. Eclipse Provided as gurobi-javadoc.jar 13

14 Platforms Added support for Python 3.6 Plus a Mac Anaconda 3.6 package Added support for Visual Studio 2017 on Windows Added support for R

15 Compute Server Statistics When remote environment is freed, we print communication statistics to the log: Compute Server communication statistics: Sent: 8.3 MBytes in 244 msgs and 0.76s (10.92 MB/s) Received: 7.2 MBytes in msgs and 1.53s (4.71 MB/s) Useful to assess overhead of Compute Server or cloud communication 15

16 New Parameters We added two additional parameters: IgnoreNames: Discard all user-provided names: constraint, variable and problem names User request to satisfy security concern when application is deployed in the cloud or using Compute Server Also useful to generate recording file to send to Gurobi support if sending names is a security concern StartNodeLimit: Provides additional control over how much work is performed to complete a partial MIP start 16

17 Additional Enhancements Improved performance of.net expression building No longer use "using" keyword in C++ header files Python: Raise AttributeError instead of GurobiError if attributes are used incorrectly printquality() also shows which variable/constraint is the maximally violated one Added Callback.useSolution() add solution immediately, and check if it is feasible Automatically flush pending changes when GRBpresolvemodel() is called Well-defined meaning of setting NodeLimit to 0 Support file compression for recording files Improved numerics in piece-wise linear simplex presolve aggregator presolve fixing of variables with almost identical bounds min/max calculation of constraint activities in presolve Gomory cuts reduced number of cases where solution exhibits small constraint violations Setting user cutoff equal to optimal value will always lead to finding such solution instead of a cutoff status 17

18 Performance Improvements 18

19 Two Kinds of Benchmarks Internal benchmarks Most important: compare Gurobi version-over-version Based on internal library of 4538 models External, competitive benchmarks Conducted by Hans Mittelmann, Arizona State University For MIP largely based upon MIPLIB

20 Internal Benchmarks 20

21 Gurobi MIP Library (4538 models) 1E ,000,000 Columns , Rows 21

22 Performance Improvements in Gurobi 7.5 Problem Class >1s >100s # Wins Losses Speedup # Wins Losses Speedup LP: concur x x primal x x dual x x barrier x x QCP/SOCP x x MIP x x MIQP x x MIQCP x x Gurobi 7.0 vs. 7.5: > 1.00x means that Gurobi 7.5 is faster than Gurobi

23 Continual Performance Improvements Number of unsolved models v1.1 v2.0 v3.0 v4.0 v5.0 v6.0 v7.0 v7.5 Time limit: sec. Intel Xeon CPU E GHz 4 cores, 8 hyper-threads 32 GB RAM MIP test set has 3420 models: discarded due to inconsistent answers discarded that none of the versions can solve 23

24 Where do the MIP improvements come from? 24

25 MIP Improvements in Gurobi 7.5 Presolve 7.8% Improved multi-row reductions 2.0% More efficient GUB usage and improved lifting 0.7% Improved domain propagation 0.4% Improved cancelation and fill handling 1.5% More lifting of knapsack constraints 0.5% Improved work limits 1.1% Extract common sub-expressions 0.4% Euclidian reductions in bound strengthening 1.0% Node presolve 4.2% Improved work limits in probing 0.3% Dual propagation for probing 0.8% Propagate dual bound 1.0% Improved conflict analysis 1.3% Exploit cliques for reduced cost strengthening 0.7% 25

26 MIP Improvements in Gurobi 7.5 Symmetry 7.0% Improved symmetry detection performance 4.1% Apply orbital probing to non-binary variables 0.7% Flip and split variables to induce symmetry 2.1% LP 17.5% Use concurrent simplex for root solve 2.6% Automatically select simplex and pricing strategy 2.5% General LP solving improvements 11.7% Cuts 14.3% Better root node control 6.3% Improved sub-mip cut separation 1.0% Improved MIR and flow cover cuts 1.9% Special odd-cycle cuts 1.3% Improved aggregation 2.8% Better clique cut separation 0.3% 26

27 MIP Improvements in Gurobi 7.5 Branching 2.8% Improved default branching strategy 2.8% Heuristics 4.6% Improved heuristics working on original model 0.9% Improved heuristics run in parallel to root cut loop 1.1% Improved work load balancing 1.4% Improved automatic adjustment of RINS fixing rate 0.5% More aggressive fix-and-dive heuristics 0.6% SOS constraints Improved SOS presolve 2.4% Node probing on SOS constraints 7.3% Improved SOS branching rules 1.4% 11.4% on SOS models 27

28 External Benchmarks Hans Mittelmann: 28

29 MIP Solve Times Gurobi 7.5 vs. Competition: Solve times > 1.0 means Gurobi faster Benchmark # CPLEX XPRESS P=1 P=4 P=12 P=48 P=1 P=4 P=12 P=48 Optimality x 1.37x 1.17x x 1.60x 1.46x - Feasibility x x - - Infeasibility x x - - "Solvable" Optim x 1.05x x 1.78x Number of solved models in solvable set P=12: Gurobi 210, Cplex 207, Xpress 197 P=48: Gurobi 211, Cplex 214, Xpress 200 Complete test data available here (data from July 20, 2017): "Optimality", time limit 7200 sec. "Feasibility", time limit 3600 sec., time to first solution "Infeasibility", time limit 3600 sec. "Solvable", time limit 7200 sec. 29

30 LP Solve Times Gurobi 7.5 vs. Competition: Solve times > 1.0 means Gurobi faster Benchmark # CPLEX XPRESS Mosek 8.0 Simplex x 1.22x 3.65x Barrier x 1.03x 2.33x Concurrent x 1.15x - Complete test data available here (data from July 20, 2017): Simplex, time limit sec., 8 threads Barrier and concurrent, time limit sec., 8 threads 30

31 Quadratic Model Solve Times Gurobi 7.5 vs. Competition: Solve times > 1.0 means Gurobi faster Benchmark # CPLEX XPRESS Mosek 8.0 SOCP x 1.26x 1.02x MISOCP x 1.12x 8.98x Binary QP x 2.13x - Convex discrete x 2.84x 4.94x Complete test data available here (data from July 20, 2017): SOCP, time limit 3600 sec., 8 threads MISOCP, time limit 7200 sec., 8 threads binary QP, time limit 3600 sec., 8 threads convex discrete, time limit 7200 sec., 8 threads 31

32 Next Steps 7.5 upgrades for existing Gurobi users Visit the What's New in Gurobi page and follow instructions in the bottom: For additional questions, or to request a free trial, contact us at info@gurobi.com 32