GAMS Model Library Subject Index. The following subject areas are covered. There is also an alphabetical index. Model. Description. Type. Authoragreste.
Agricultural Farm Level Model of NE Brazil. LPKutcher, G Pchance. Chance Constrained Feed Mix Problem.
NLPBracken, Jchina. Organic Fertilizer Use in Intensive Farming. LPWiens, T Bdemo. Simple Farm Level Model. LPKutcher, G Pdemo.
Nonlinear Simple Agricultural Sector Model. NLPKutcher, G Pegypt. Egypt Agricultural Model. LPKutcher, G Pindus. Indus Agricultural Model. LPDuloy, J Hindus. Indus Basin Water Resource Model.
I've been wondering if there are known solutions for algorithm of creating a school timetable. Basically, it's about optimizing 'hour-dispersion' (both in teachers. 2013 crack software download. Please press Ctrl+F to find your cracked software you needed.-----I have the. 4 Solving Linear Programming Problems: The Simplex Method. We now are ready to begin studying the simplex method, a general procedure for solving linear programming.
LPAhmad, Miswnm. Indus Surface Water Network Submodule. LPDuloy, J Hnebrazil. North- East Brazil Regional Agricultural Model.
LPKutcher, G Ppaklive. Pakistan Punjab Livestock Model. LPWorld Bankqdemo. Nonlinear Simple Agricultural Sector Model QCPQCPKutcher, G Psaras.
Model: Description: Type: Author: alkyl: Simplified Alkylation Process: NLP: Berna, T J: batchdes: Optimal Design for Chemical Batch Processing: MINLP: Kocis, G R: catmix. IBM ILOG CPLEX Optimization Studio Getting Started with CPLEX Version 12 Release 6. No more missed important software updates! UpdateStar 11 lets you stay up to date and secure with the software on your computer.
South African Regionalised Farm- level Resource Use and Output Supply Response (SARAS) model. NLPOlubode- Awosola, O Osarf. Farm Credit and Income Distribution Model. LPHusain, Tturkey.
Turkey Agricultural Model with Risk. NLPLe- Si, VModel. Description. Type. Authorcafemge. Corporate average fuel economy standards. MPSGEThorpe, Scamcge. Cameroon General Equilibrium Model Using NLPNLPCondon, Tcamcns. Cameroon General Equilibrium Model Using CNSCNSCondon, Tcammcp.
Cameroon General Equilibrium Model Using MCPMCPCondon, Tcammge. Cameroon General Equilibrium Model Using MPSGEMPSGECondon, Tcirimge. Increasing returns in intermediate inputs. MPSGELopez de Silanes, Fdecomphh. A Successive Recalibration Algorithm for GE Models with Many Households. MPSGERutherford, T Fdyncge.
A Recursive- Dynamic Standard CGE Model. NLPHosoe, Ners. 82mcp. USDA- ERS CGE Model of the USMCPRobinson, Sfinmge. A General Equilibrium Model for Finland. MPSGETorma, Hgancns.
Macro- Economic Framework for India - CNSCNSMitra, P Kgancnsx. Macro- Economic Framework for India - Tracking CNSCNSMitra, Pganges. Macroeconomic Framework for India. NLPMitra, P Kgangesx. Economic Framework for India - Tracking. NLPMitra, Pgemmcp. GEMTAP: A general equilibrium model for tax policy.
MCPRutherford, T Fgemmge. GEMTAP: A general equilibrium model for tax policy. MPSGEBalard, C Lhansmcp.
Hansen's Activity Analysis Example - MCPMCPScarf, Hhansmge. Hansen's Activity Analysis Example - MPSGEMPSGEScarf, Hharmge. Increasing returns with average cost pricing. MPSGEKamiya, Khhmax. A Household Maximization Problem.
NLPHosoe, Nirscge. A CGE Model with Scale Economy. NLPHosoe, Nkehomge.
Multiple equilibria in a simple GE model. MPSGEKehoe, Tkorcge. General Equilibrium Model for Korea - NLPNLPLewis, Jkorcns. General Equilibrium Model for Korea - CNSCNSLewis, Jkormcp.
General Equilibrium Model for Korea - MCPMCPLewis, Jlrgcge. A Large Country CGE Model. NLPHosoe, Nmoncge. A Monopoly CGE Model. NLPHosoe, Nmr. 5mcp.
Multi- Region Growth Model Based on Global 2. MCPManne, A Snash. A non- cooperative game: Nash and Stackelberg versions. MPECMurphy, F Holigomcp. Oligopolistic Competition - Examples from MPMCPHarker, P Tprisoner. Prisoners dilemma as EMP and MCPEMPFerris, M Cquocge. A CGE Model with Quotas.
NLPHosoe, Nsammge. Carbon taxes with exemptions for heavy industry. MPSGEBoehringer, Cscarfmcp. Scarf's Activity Analysis Example. MCPScarf, Hscarfmge. Tax distortions in a small activity analysis model.
MPSGEScarf, Hshovmge. The economic effects of UK membership in the ECMPSGEMiller, M Hspatequ. Spatial Equilibrium. MCPHieu, P Ssplcge. A Simple CGE Model. NLPHosoe, Nstdcge.
A Standard CGE Model. NLPHosoe, Nthreemge.
Three Approaches to Differential Tax Policy Analysis. MPSGERutherford, T Ftwocge. A Two Country CGE Model. NLPHosoe, Ntwo. 3mac. Simple 2 x 2 x 2 General Equilibrium Model Using Macros.
MCPShoven, Jtwo. 3mcp. Simple 2 x 2 x 2 General Equilibrium Model.
MCPShoven, Junstmge. Globally Unstable Equilibria. MPSGEScarf, Hvonthmcp. General Equilibrium Variant of the von Thunen Model. MCPRowsevonthmge.
A General Equilibrium Version of the von Thunen Model. MPSGEMackinnon, JModel.
Description. Type. Authorbchfcnet. Fixed Cost Network Flow Problem with Cuts using BCH Facility. MIPBussieck, M Rbchmknap.
Multi knapsack problem using BCH Facility. MIPBeasley, J Ebchoil. Oil Pipeline Design Problem using BCH Facility. MIPBrimberg, Jbchstock. Cutting Stock - A Column Generation Approach with BCHMIPGilmore, P Cbchtlbas.
Trim Loss Minimization with Heuristic using BCH Facility. MINLPFloudas, C Abchtsp. Traveling Salesman Problem Instance with BCHMIPBeasley, J EModel. Description. Type. Authoralkyl. Simplified Alkylation Process. NLPBerna, T Jbatchdes.
Optimal Design for Chemical Batch Processing. MINLPKocis, G Rcatmix.
Catalyst Mixing COPS 2. NLPDolan, E Dchem. Chemical Equilibrium Problem. NLPBracken, Jcsched.
Cyclic Scheduling of Continuous Parallel Units. MINLPAnd, V Jfeedtray. Optimum Feed Plate Location.
MINLPViswanathan, Jgasoil. Catalytic cracking of gas oil COPS 2. NLPAscher, U Mhaverly.
Haverly's pooling problem example. NLPAdhya, Nhda. Synthesis: Hydrodealkylation of Toluene. MINLPKocis, G Rkport. Product Portfolio Optimization. MINLPKallrath, Jmethanol. Methanol to hydrocarbons COPS 2. NLPAscher, U Mminlphi.
Heat Integrated Distillation Sequences. NLPFloudas, C Aminlphix. Heat Integrated Distillation Sequences. MINLPFloudas, C Anonsharp. Synthesis of General Distillation Sequences. MIPAggarwal, Ansharpx.
Synthesis of General Distillation Sequences. MINLPAggarwal, Apinene. Isometrization of alpha- pinene COPS 2. NLPAscher, U Mpool.
Pooling problem. NLPAdhya, Nprocess. Alkylation Process Optimization. NLPBracken, Jprocsel.
Structural Optimization of Process Flowsheets. MINLPKocis, G Rreaction. Logical Inference for Reaction path synthesis. MIPRaman, Rsynheat. Simultaneous Optimization for Hen Synthesis. MINLPYee, T Ftanksize. Tank Size Design Problem.
MINLPRebennack, Swall. Chemical Equilibrium Problem.
NLPWall, T Wwallmcp. Chemical Equilibrium Problem as MCPMCPWall, T WModel. Description. Type. Authorpartssupply. Parts Supply Problem.
NLPHashimoto, Hps. Parts Supply Problem w/ 1.
Types and w/ Asymmetric Information. NLPHashimoto, Hps. Parts Supply Problem w/ 1. Types w/ Random p(i)NLPHashimoto, Hps.
Parts Supply Problem w/ 2 Types w/o and w/ Asymmetric Information. NLPHashimoto, Hps. Parts Supply Problem w/ 2 Types w/o Asymmetric Information. NLPHashimoto, Hps. Parts Supply Problem w/ Efficient Type w/o Asymmetric Information. NLPHashimoto, Hps. Parts Supply Problem w/ Inefficient Type w/o Asymmetric Information.
NLPHashimoto, Hps. Parts Supply Problem w/ 2 Types w/ Asymmetric Information. NLPHashimoto, Hps.
Parts Supply Problem w/ 3 Types w/o Asymmetric Information. NLPHashimoto, Hps. Parts Supply Problem w/ 3 Types w/ Asymmetric Information. NLPHashimoto, Hps. Parts Supply Problem w/ 3 Types w/ Global Incentive Comp.
Const. NLPHashimoto, Hps. Parts Supply Problem w/ 3 Types w/ Monotonicity Constraint.
NLPHashimoto, Hps. Parts Supply Problem w/ 3 Types w/o and w/ SCPNLPHashimoto, Hps. Parts Supply Problem w/ 5 Types w/ Random p(i)NLPHashimoto, HModel. Description. Type.
Authorleast. Nonlinear Regression Problem. NLPBracken, Jlike. Maximum Likelihood Estimation. NLPBracken, Jlinear. Linear Regression with Various Criteria. DNLPBracken, Jmws.
Computation of Horowitz's work- trip mode choice model estimates. MIPFlorios, KModel. Description. Type. Authorchenery. Substitution and Structural Change. NLPChenery, H Bdinam.
DINAMICO A Dynamic Multi- Sectoral Multi- Skill Model. LPManne, A Sdmcmge. Accounting for economic growth with new inputs. MPSGEFeenstra, R Cpak. Optimal Patterns of Growth and Aid. LPChenery, H Bprolog.
Market Equilibrium and Activity Analysis. NLPNorton, R Dramsey.
Savings Model by Ramsey. NLPMurtagh, B AModel. Description. Type. Authorlogmip. 1a. Log. MIP User's Manual Example 1a - Job Scheduling.
EMPRaman, Rlogmip. Log. MIP User's Manual Example 1b - Job Scheduling. EMPRaman, Rlogmip.
Log. MIP User's Manual Example 1c - Job Scheduling. EMPRaman, Rlogmip.
Log. MIP User's Manual Example 2 - Example to illustrate disjunctions. EMPVecchietti, Alogmip. Log. MIP User's Manual Example 3 - Synthesis of 8 Processes. EMPRaman, Rlogmip. Log. MIP User's Manual Example 4 - Job Shop Scheduling. EMPRaman, RModel. Description. Type.
Authoretamac. ETA- MACRO Energy Model for the USANLPManne, A Setamge. ETA- MACRO Energy Model for the USA - MPSGE Format. MPSGEManne, A Sgtm.
International Gas Trade Model. NLPManne, A Skorpet. Investment Planning in the Korean Oil- Petro Industry. MIPSuh, J Slaunch. Launch Vehicle Design and Costing. NLPBracken, Jlinearne. Linearization techniques for extremal- Nash equilibria.
EMPHesamzadeh, M Rmarkov. Strategic Petroleum Reserve. LPTeisberg, T Jotpop. OPEC Trade and Production.
NLPBlitzer, Cpindyck. Optimal Pricing and Extraction for OPECNLPPindyck, R Spollut. Industrial Pollution Control. NLPMangasarian, O Lpoutil. Portfolio Optimization for Electric Utilities.
MIPRebennack, Sshale. Investment Planning in the Oil Shale Industry. LPMelton, J Wsrcpm. Single- Region Contingency Planning Model.
NLPManne, A Stabora. Tabora Rural Development - Fuelwood Production. LPWorld Bankturkpow. Turkey Power Planning Model. LPTurvey, RModel.
Description. Type. Authorbearing. Hydrostatic Thrust Bearing Design for a Turbogenerator. NLPCoello Coello, C Acamshape. Shape optimization of a cam COPS 2. NLPAnitescu, Mchain.
Hanging Chain COPS 2. NLPCesari, Ldispatch. Economic Load Dispatch Including Transmission Losses. NLPWood, A Jelec. Distribution of electrons on a sphere COPS 2. NLPDolan, E Dfdesign.
Linear Phase Lowpass Filter Design. QCPLobo, M Sflowchan. Flow in a channel COPS 2.
NLPAscher, U Mfuel. Fuel Scheduling and Unit Commitment Problem. MINLPWood, A Jgasnet. Optimal Design of a Gas Transmission Network. MINLPEdgar, T Fgastrans.
Gas Transmission Problem - Belgium. NLPde Wolf, Dgear. Gear Train Design. MINLPDeb, Kglider. Hang glider COPS 2.
NLPBulirsch, Rherves. Herves (Transposable Element) Activity Calculations. DNLPMeeraus, W Hhydro.
Hydrothermal Scheduling Problem. NLPWood, A Jjbearing.
Journal bearing COPS 2. NLPAverick, B Mlnts. Particle steering COPS 2. NLPBetts, Jminsurf. Minimal surface with obstacle COPS 2.
NLPDolan, E Dpump. Pump Network Synthesis. MINLPFloudas, C Arobot.
Robot arm COPS 2.
IBM CPLEX Performance Tuning for Linear Programs. CPLEX has internal logic that enables it to examine a linear program (LP) and typically make good decisions regarding the best way to solve it. As a result, CPLEX's default settings typically perform very well. However, if you do need to improve CPLEX's performance on a linear program, here are some suggestions. Refer to the CPLEX User Manual if you need additional information on any of the concepts mentioned, and refer to the CPLEX Parameters Reference Manual for more information on any of the individual parameters and their associated settings discussed below. Try each linear programming algorithm. Solving the dual problem may be faster. Check if degeneracy hampers performance.
Check for numerical difficulties. Try each linear programming algorithm.
While changing individual parameters rarely has a major impact on LP performance, CPLEX's algorithms can behave quite differently on a problem. CPLEX can solve a linear program using the primal simplex method, the dual simplex method, the barrier method, and, in some cases, the network simplex method. CPLEX also supports variants of these algorithms including sifting and concurrentopt. Sifting is a simple form of column generation well suited for models where the number of variables dramatically exceeds the number of constraints. Concurrentopt works with multiple processors, running a different algorithm on each processor and stopping as soon as one of them finds an optimal solution. Find out which algorithm works best on your particular problem if you need to improve performance. The barrier method tends to work well on problems where the product of the constraint matrix multiplied by its transpose is sparse. Solving the dual problem may be faster. You can obtain the solution to a linear program either by solving the primal linear program itself, or solving the dual of the linear program and using the dual's dual variables to obtain the solution to the linear program. If you set the presolve dual indicator on, CPLEX will do this for you by taking the dual problem, solving it, and returning solution values in the context of the primal problem. Since the computation time for the simplex method depends more on the number of constraints than the number of variables, this approach is most likely to help on problems where the primal has more constraints than variables. The dual will therefore have fewer constraints. CPLEX will probably solve it faster than the primal., especially if you try each linear programming algorithm as mentioned above.
Check if degeneracy hampers performance. Degeneracy can dramatically slow down the simplex method. If a customer reports slow performance on a linear program, degeneracy is a likely cause. To verify this, check the CPLEX iteration log and look for long sequences of iterations where the objective value remains unchanged. Also, look for messages that indicate CPLEX has to perturb the problem. While perturbations help speed up performance on degenerate problems, using a different algorithm works better. If the problem is primal degenerate, the dual simplex method may still work well. If the problem is dual degenerate, the primal simplex may still work well. If the problem is both primal and dual degenerate, the barrier method may work well. Check for numerical difficulties.
A computer is a finite precision machine. CPLEX's algorithms perform millions of floating point computations, and round off error can gradually accumulate. CPLEX uses numerically stable methods to perform its linear algebra, so round off error usually does not cause problems. However, if a problem is fundamentally ill conditioned, even the most stable methods may have trouble, either in terms of the accuracy of the final solution or simply the amount of time required to solve the problem. Make good use of CPLEX's solution quality routines to determine if this is a problem. From the Interactive CPLEX Optimizer, use the 'display solution quality' command. Also, 'display solution kappa' will give the condition number of the current basis. Condition numbers larger than 1e+1. From the CPLEX Callable Library, use the CPXgetdblquality and CPXgetkappa routines to obtain the corresponding information. From Concert, use Ilo. Cplex: :get. Quality for solution quality. Concert doesn't currently support a function to obtain the basis condition number. If you do find that numerical instability hampers performance, setting the Markowitz tolerance to a value of . Also, try setting CPLEX's scaling parameter to 1. Users of CPLEX 1.
CPLEX's numerical caution emphasis parameter on to set these (and other) parameters all at once. Also, consider the feasibility and optimality tolerances. The default values are 1e- 6. If your problem data is only accurate to a larger value (e.