| Testcases | LP-Testcases |
The classical collection of mostly real-life problems is at NETLIB. The files are compressed with a special utility (MPC), they need to be uncompressed using EMPS. Then they are in MPS-format  . The original source for the files and the (un)compress utilities is netlib. See the README. A utility to convert MPS to and from LP-format is available at LP2MPS. One can also retrieve the files in AMPL-format from NETLIB_AMPL. A comprehensive archive for files in different formats is the COAP collection.
Here is another large collection of mpc-compressed MPS files and here are some additional files. In practice often some or all of the variables are constrained to be integer valued. Here is a collection of mixed integer linear programming problems: There are more codes for unconstrained problems in the general collections listed below. | Testcases for semidefinite and second-order cone programming |  | SDPLIB | SDP test problems in sparse SDPA-format | | SQL | SQL problems (DIMACS Challenge), SeDuMi and DIMACS graph formats, in sparse SDPA format | | KOCVARA | sparse SDP's from structural optimization (in sparse SDPA format) (in Matlab binary format) | | ESC | SDPs from electronic structure calculations (in sparse SDPA format) | | QAP | SDP relaxations of QAP problems by Rendl, Sotirov, and Wolkowicz (in sparse SDPA and Matlab binary format) | | SOCP | Second-order cone problems from 7th DIMACS Challenge (in extended MPS format) | | SDP | More SDP problems (in sparse SDPA format) |
| Testcases for general nonlinear programming |
The following collection is written in standard f77 with milstd1753 extensions. It uses a problem formulation for nonlinear programming f(x)=min subject to h(x)=0 and g(x)>=0, where h and g are general smooth vector functions. there is also an interface for the format used e.g. by codes like NPSOL, MINOS and SNOPT. The collection contains all examples of the two collections assembled by Schittkowski resp. Hock and Schittkowski, most of Himmelblau and Dembo and some additional ones. The code DONLP2 solves them all but (purposely) one successfully. | Testcases for PDE constrained optimization |
| Testcases for parameter estimation |
| Testcases for multidisciplinary optimization | | MCDM | MCDM Numerical Instances Library | | MDO Test Suite | NASA Langley's problem library |
| Testcases for various discrete optimization problems | | MP-Testdata at ZIB | | | Multicommodity problems | problems, generators, format converter (C++) | | frequency assignment problems | benchmarks, other info on FAP | | TSPLIB | library of traveling salesman, Hamiltonian cycle, sequential ordering, and capacitated vehicle routing problems | | VRPTW | Extended Solomon's VRPTW instances | | LOLIB | library of linear ordering problems | | BINPP | 1-d bin packing and paper on exact algorithm | | UNIBO | Bin-packing, general MIPs and others | | QAPLIB | QAP Library and related links | | SATLIB | SAT benchmarks, solvers, links etc. | | CSPLIB | a problem library for constraints | | SteinLib | a collection of Steiner tree problems in graphs | | PSPLIB | Project Scheduling Library | | Taillard's instances | QAP, Scheduling, VRP | | P&S | Planning and Scheduling Benchmarks | | FacLoc | testdata for various facility location problems | | OR-Library | testdata for a variety of OR problems | | 0-1 Constraint Satisfaction Benchmarks | realistic cases in various formats | | Resende's collection | of Max-SAT, Steiner triple and other problem data sets | | Problem Instances | from the Informs Resource Collection |
The error free formulation of large probems by direct coding in some programming language is fatiguing. Special coding devices are of great help here. The SIF (=standard input format) developed by Conn, Gould and Toint is one of them. The following collection contains nearly a thousand problems (with the additional possibility to vary dimension) coded in SIF. The selection tool allows you to extract subcollections of specific properties. | Testcases coded in special format | | CUTEr | Constrained and Unconstrained Testing Environment | | | including large scale testcases, in SIF format | | Select problems | from CUTE with desired characteristics |
AMPL and GAMS are modelling languages which allow a user to formulate problems in terms very near the original problem and transform this into a format required by specific solvers via specialized interfaces of which no knowledge is required by the user. They also provide automatically analytic derivatives.
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