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AIMMS Language Reference - Mixed Complementarity Problems This ﬁle contains only one chapter of the book. For a free down load of the complete book in pdf format, please visit www.aimms.com or order your hard- copy at www.lulu.com/aimms Aimms 3.13

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Copyright 1993–2012 by Paragon Decision Technology B.V. All rights re served. Paragon Decision Technology B.V. Schipholweg 1 2034 LS Haarlem The Netherlands Tel.: +31 23 5511512 Fax: +31 23 5511517 Paragon Decision Technology Inc. 500 108th Avenue NE Ste. # 1085 Bellevue, WA 98004 USA Tel.: +1 425 458 4024 Fax: +1 425 458 4025 Paragon Decision Technology Pte. Ltd. 55 Market Street #10-00 Singapore 048941 Tel.: +65 6521 2827 Fax: +65 6521 3001 Paragon Decision Technology Shanghai Representative Oﬃce Middle Huaihai Road 333 Shuion Plaza, Room 1206 Shanghai China Tel.: +86 21 51160733 Fax: +86 21 5116 0555 Email: info@aimms.com WWW: www.aimms.com Aimms is a registered trademark of Paragon Decision Technology B. V. IBM ILOG CPLEX and CPLEX is a registered trademark of IBM Corporation. GUROBI is a registered trademark of Gurobi Optimization, Inc. KNITRO is a registered trademark of Ziena Optimization, Inc. XPRESS-MP is a registered trademark of FICO Fair Isaac Corporation. Mosek is a registered trademark of Mosek ApS. Windows and Excel are registered trademarks of Microsoft Corporation. T X, L X, and -L X are trademarks of the American Mathematical Society. Lucida is a registered trademark of Bigelow & Holmes Inc. Acrobat is a registered trademark of Adobe Systems Inc. Other brands and their produ cts are trademarks of their respective holders. Information in this document is subject to change without no tice and does not represent a commitment on the part of Paragon Decision Technology B.V. The software de scribed in this document is furnished under a license agreement and may only be used and copied in accorda nce with the terms of the agreement. The documentation may not, in whole or in part, be copied, photoc opied, reproduced, translated, or reduced to any electronic medium or machine-readable form without pri or consent, in writing, from Paragon Decision Technology B.V. Paragon Decision Technology B.V. makes no representation o r warranty with respect to the adequacy of this documentation or the programs which it describes for any particular purpose or with respect to its adequacy to produce any particular result. In no event shall Paragon Decision Technology B.V., its employees, its contractors or the authors of this docume ntation be liable for special, direct, indirect or consequential damages, losses, costs, charges, claims, demands, or claims for lost proﬁts, fees or expenses of any nature or kind. In addition to the foregoing, users should recognize that al l complex software systems and their doc- umentation contain errors and omissions. The authors, Para gon Decision Technology B.V. and its em- ployees, and its contractors shall not be responsible under any circumstances for providing information or corrections to errors and omissions discovered at any tim e in this book or the software it describes, whether or not they are aware of the errors or omissions. The a uthors, Paragon Decision Technology B.V. and its employees, and its contractors do not recommend the use of the software described in this book for applications in which errors or omissions could thr eaten life, injury or signiﬁcant loss. This documentation was typeset by Paragon Decision Technol ogy B.V. using L X and the Lucida font family.

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Chapter 18 Mixed Complementarity Problems This chapter This chapter discusses the special identiﬁer types and lang uage constructs that Aimms oﬀers to allow you to formulate mixed complementarity probl ems. Although mixed complementarity problems do not involve opt imization, they are speciﬁed through variables which are linked to constrai nts in terms of these variables, and thus fall into the common framework of a MATHEMATICAL PROGRAM Aimms also supports nonlinear optimization problems with additi onal complementarity constraints (also known as MPCC or MPEC pro blems) 18.1 Complementarity problems Complementar- ity problems Complementarity relations arise in a variety of engineerin g and economics ap- plications, most commonly to express an equilibrium of quan tities such as forces or prices. One standard application in engineering a rises in contact me- chanics, where complementarity expresses the fact that fri ction occurs only when two bodies are in contact. Other applications are found in structural mechanics, structural design, traﬃc equilibrium and optim al control. Economic models Interest among economists in solving complementarity prob lems is due in part to increased use of computational general equilibrium mode ls, where for in- stance complementarity is used to express Walras’ Law, and i n part to the equivalence of various games to complementarity problems. Nonlinear optimization Some generalizations of nonlinear programming, such as mul ti-level optimi- zation—in which auxiliary objectives are to be minimized—m ay be reformu- lated as problems with complementarity conditions. Also, b y formulating the Kuhn-Tucker conditions of a nonlinear optimization model o ne obtains a com- plementarity problem, which could be solved by a complement arity solver. In the latter case, however, one requires second-order deriva tive information of all constraints in the original optimization model.

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Chapter 18. Mixed Complementarity Problems 258 Complementar- ity conditions Complementarity problems are, in general, systems of nonli near constraints where variables in the system are linked to constraints in th e form of comple- mentarity conditions. There are two forms of complementari ty conditions, the classical complementarity condition, and its generalizat ion, the mixed comple- mentarity condition. Classical com- plementarity conditions The classical form of a complementarity condition involves a nonnegative vari- able and an associated function (x) . It requires that 0 and (x) or 0 and (x) This condition states that of both inequalities, at least on e must reach its bound. Alternatively, one can formulate this complementar ity condition as 0, (x) 0 and (x) 0. Mixed comple- mentarity condition The mixed form of a complementarity condition involves a bou nded variable with an associated function (x) . It requires that and (x) or and (x) or and (x) This condition states that either must reach one of its bounds, or the func- tion (x) must be zero. A mixed complementarity condition can be split into two classical complementarity conditions (albeit by intro ducing auxiliary vari- ables). The classical complementarity condition, on the ot her hand, is a special case of the mixed complementarity condition by choosing 0 and = Special cases By choosing = and = , the mixed complementarity condition re- duces to the special case is “free” and (x) Another special case is obtained when . The mixed complementarity condition then reduces to and (x) is “free Supported com- plementarity conditions in Aimms All complementarity conditions described above can be repr esented by asso- ciating a variable with a single constraint, which will form the basis for repre- senting complementarity conditions in Aimms . Consider the inequalities and (x) where (x) is a nonlinear expression, and exactly two of the constants and are ﬁnite. The six possible cases are enumerated in Table 18.1 and are discussed below.

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Chapter 18. Mixed Complementarity Problems 259 Case ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite Table 18.1: Six allowed cases with exactly two ﬁnite bounds Case 1 The case and (x) corresponds to the mixed complementarity condition already discussed above: and (x) or and (x) or and (x) Case 2 The case and (x) corresponds to the classical com- plementarity condition 0 and (x) or 0 and (x) where and (x) (x) Case 3 The case and (x) corresponds to the classical complementarity condition 0 and (x) or 0 and (x) where and (x) (x) Case 4 The case and (x) corresponds to the classical complementarity condition 0 and (x) or 0 and (x) where and (x) (x)

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Chapter 18. Mixed Complementarity Problems 260 Case 5 The case and (x) corresponds to the classical complementarity condition 0 and (x) or 0 and (x) where and (x) (x) Case 6: The case and (x) with corresponds to the ﬁrst special case of the mixed complementarity condition is “free” and (x) where f(x) (x) Case 6: After the introduction of variables ,x 0 and functions (x) (x) (x) (x) (x) the case and (x) with corresponds to a system of three simultaneous complementarity conditions is “free” and (x) 0 and (x) or 0 and (x) 0 and (x) or 0 and (x) Aimms support Aimms supports the variable-constraint couples with two ﬁnite bo unds, as dis- cussed above, through the special COMPLEMENTARITY VARIABLE data type. The declaration and attributes of this data type are discussed i n the next section, while section 18.3 describes the declaration of mixed complementarity models through the common MATHEMATICAL PROGRAM declaration. Well-behaved systems Like with nonlinear optimization models, not all mixed comp lementarity sys- tems that can be formulated are well-behaved. For instance, a variable with an associated constraint 1 0, only admits the solutions 0 and 1, which would destroy the continuous character of complement arity problems. For systems of complementarity conditions that are not well -behaved, the so- lution process may produce no, or unexpected results.

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Chapter 18. Mixed Complementarity Problems 261 18.2 COMPLEMENTARITY VARIABLE declaration and attributes Complementar- ity variables To support you in formulating a complementarity model, Aimms provides a special type of variable, the COMPLEMENTARITY VARIABLE . The attributes of a com- plementarity variable allow you to declare an (indexed) cla ss of variables in a complementarity model along with their associated constra ints. The attributes of a COMPLEMENTARITY VARIABLE are listed in Table 18.2 Automatic sanity checks By construction, this new variable type automatically ensu res that every vari- able in a complementarity model is associated with a single c onstraint. Also, when Aimms detects that the total number of (ﬁnite) bounds on both the co m- plementarity variable and its associated constraint is not equal to two (as re- quired above), a compilation error will result. Thus, COMPLEMENTARITY VARIABLES will help to reduce the most common declaration errors for th is type of model. Attribute Value-type See also page INDEX DOMAIN index-domain 42 208 208 RANGE range 208 UNIT unit-valued expression 45 211 TEXT string 19 45 COMMENT comment string 19 COMPLEMENT expression 217 NONVAR STATUS reference 212 PROPERTY NoSave Complement Table 18.2: COMPLEMENTARITY VARIABLE attributes The INDEX DOMAIN attribute Through the INDEX DOMAIN attribute of a complementarity variable you can specify domain of tuples for which you want Aimms to generate a variable and its associated constraint. During generation, Aimms will only generate a vari- able for all tuples that satisfy all domain restrictions tha t you have imposed on the domain. The RANGE attribute In the RANGE attribute you can specify the lower and upper bound of a com- plementarity variable, in a similar manner for ordinary VARIABLES (see also Sec- tion 14.1 ). During generation, Aimms will perform a runtime check, for every individual tuple in the index domain, whether the number of nite bounds speciﬁed here, plus the number of ﬁnite bounds in the constra int speciﬁed in the COMPLEMENT attribute, exactly equals two.

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Chapter 18. Mixed Complementarity Problems 262 The COMPLEMENT attribute The COMPLEMENT attribute allows you to specify the constraint that must be a s- sociated with the complementarity variable at hand. With f(x,...) a general nonlinear function, the following types of expressions are allowed f(x,...) (variable must have a single-sided RANGE ), f(x,...) (variable must have a single-sided RANGE ), f(x,...) (variable must be free), f(x,...) (variable must be free), or f(x,...) (variable must be bounded). In addition, the COMPLEMENT attribute can hold a reference to an existing CON- STRAINT in your model, which then should hold a deﬁnition as one of the cases above. The COMPLEMENT attribute can also hold a scalar element parameter into the set AllConstraints , which oﬀers the possibility to assign diﬀerent con- straints to the complementarity variable in sequential sol ves. Constraint listing In the constraint listing, the constraints associated with a complementarity variable will be listed with a generated name consisting of t he name of the COMPLEMENTARITY VARIABLE with an additional suﬃx complement ”. The NONVAR STATUS attribute With the NONVAR STATUS attribute you can indicate for which tuples you want Aimms to consider the complementarity variable as a parameter, i. e. with the lower and upper bound set equal to the level value prior to sol ving the model (see also Section 14.1.1 ). From the mixed complementarity condition it follows that the function in the corresponding constraint is then al lowed to assume arbitrary values, whence there is no strict need to generate the variable and constraint for the solver. Positive and negative values The value of the NONVAR STATUS attribute must be an expression in some or all of the indices in the index list of the variable, allowing you to change the non- variable status of individual elements or groups of element s at once. When the NONVAR status assumes a positive value, Aimms will not generate the variable and its associated constraint. For negative values, the var iable and constraint will be generated, but reduces to the second special case of t he mixed comple- mentarity condition 0 and (x) is “free i.e. the function in the constraint will be allowed to assume arbitrary values. The UNIT attribute Providing a UNIT for a complementarity variable will help you in a number of ways. Aimms will help you to check the consistency of all the constraints and assignments in your model (including the expression in the COMPLEMENT attribute), and Aimms will use the units to scale the model that is sent to the solver

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Chapter 18. Mixed Complementarity Problems 263 Proper scaling of a model will generally result in a more accu rate and robust solution process. You can ﬁnd more information on the deﬁnit ion and use of units to scale mathematical programs in Chapter 31 The PROPERTY attribute Complementarity variables support the properties NoSave and Complement . With the property NoSave you indicate that you do not want to store data associated with this variable in a case. The Complement property indicates that you are in- terested in the level values of the constraint deﬁned in the COMPLEMENT attribute. When this property is set, Aimms will make the level value of this constraint available through the .Complement suﬃx of the complementarity variable at hand. Example The declaration of the complementarity variable MembraneHeight expresses a complementarity condition for the height of a membrane in a r ectangular (x,y) -grid, with a uniform external force acting on each cell in th e grid. COMPLEMENTARY VARIABLE: identifier : MembraneHeight index domain : (x,y) range : [MembraneLowerBound(x,y), MembraneUpperBound(x ,y)] complement : 4*MembraneHeight(x,y) - MembraneHeight(x+1,y) - MembraneHeight(x-1,y) - MembraneHeight(x,y+1) - MembraneHeight(x,y-1) - CellForce ; The complementarity condition expresses that either the me mbrane reaches one its given bounds (for instance, an obstacle placed in the way of the mem- brane), or the external force on the cell must be equal to the i nternal forces acting on the cell caused by diﬀerences in height with neighb oring cells. 18.3 Declaration of mixed complementarity models Mixed comple- mentarity models To deﬁne a pure mixed complementarity model, you must declar e a MATHEMA- TICAL PROGRAM (see also Section 15.1 ) and specify mcp as the TYPE attribute of the MATHEMATICAL PROGRAM . In the VARIABLES attribute you can specify a subset of the set of all COMPLEMENTARITY VARIABLES to be included in the mixed comple- mentarity model at hand. Based on this speciﬁcation, Aimms will automatically generate all constraints associated with these complement arity variables, re- sulting in a square system. Additional variables and constraints In addition, Aimms allows you to add ordinary variables to the VARIABLES at- tribute, and to specify additional constraints in the CONSTRAINTS attribute of the MATHEMATICAL PROGRAM that must be satisﬁed as well. If the solver used to solve the mixed complementarity model requires a square sys tem, Aimms will automatically add auxiliary constraints or variables to th e generated system,

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Chapter 18. Mixed Complementarity Problems 264 and provide the linkages with the ordinary variables and con straints you have added to the system. No optimization For a mixed complementarity problem you should not specify t he OBJECTIVE and DIRECTION attributes, as a complementarity solver will only compute a fea- sible solution that satisﬁes all the complementarity condi tions speciﬁed. If these attributes are not empty, Aimms will produce a runtime error when you apply the SOLVE statement the corresponding MATHEMATICAL PROGRAM (see also Section 15.3 ). Example A mixed complementarity model containing the declaration o f the comple- mentarity variable MembraneHeight declared in the previous section, is deﬁned by the following declaration. MATHEMATICAL PROGRAM: identifier : Membrane variables : AllVariables type : mcp ; As usual, you can solve the Membrane through the statement solve Membrane; which will generate the mixed complementarity model and inv oke a suitable solver for mcp problem type. 18.4 Declaration of MPCC models MPCC models Through the knitro solver, Aimms also supports m athematical p rograms with omplementarity c onstraints (MPCC models). MPCC models are also more com- monly denoted by other modeling languages as MPEC models, wh ich form a more general, and much more diﬃcult, class of optimization p roblems. A MPCC model is an ordinary NLP model with additional compleme ntarity con- straints that have to be satisﬁed. Declaring MPCC models To deﬁne a MPCC model, you must declare a MATHEMATICAL PROGRAM (see also Section 15.1 ) and specify mpcc as the TYPE attribute of the MATHEMATICAL PROGRAM The variable set of a MATHEMATICAL PROGRAM of a MPCC model can contain ordi- nary variables as well as complementarity variables. Contr ary to pure mixed complementary models, a MPCC model has an objective functio n.

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Chapter 18. Mixed Complementarity Problems 265 Solving MPCC models To solve MPCC models in Aimms , you need a license for the knitro solver. If you do not have a license for the knitro solver, Aimms will return an error that it has no suitable solver available for the mpcc class, whenever you try to solve a MPCC model. The knitro solver can also be used for solving pure mixed complementarity problems, but is, in general, far les s eﬃcient in that case than dedicated mcp solvers.

For a free down load of the complete book in pdf format please visit wwwaimmscom or order your hard copy at wwwlulucomaimms Aimms 313 brPage 2br Copyright 19932012 by Paragon Decision Technology BV All rights re served Paragon Decision Technology BV ID: 23026

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AIMMS Language Reference - Mixed Complementarity Problems This ﬁle contains only one chapter of the book. For a free down load of the complete book in pdf format, please visit www.aimms.com or order your hard- copy at www.lulu.com/aimms Aimms 3.13

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Copyright 1993–2012 by Paragon Decision Technology B.V. All rights re served. Paragon Decision Technology B.V. Schipholweg 1 2034 LS Haarlem The Netherlands Tel.: +31 23 5511512 Fax: +31 23 5511517 Paragon Decision Technology Inc. 500 108th Avenue NE Ste. # 1085 Bellevue, WA 98004 USA Tel.: +1 425 458 4024 Fax: +1 425 458 4025 Paragon Decision Technology Pte. Ltd. 55 Market Street #10-00 Singapore 048941 Tel.: +65 6521 2827 Fax: +65 6521 3001 Paragon Decision Technology Shanghai Representative Oﬃce Middle Huaihai Road 333 Shuion Plaza, Room 1206 Shanghai China Tel.: +86 21 51160733 Fax: +86 21 5116 0555 Email: info@aimms.com WWW: www.aimms.com Aimms is a registered trademark of Paragon Decision Technology B. V. IBM ILOG CPLEX and CPLEX is a registered trademark of IBM Corporation. GUROBI is a registered trademark of Gurobi Optimization, Inc. KNITRO is a registered trademark of Ziena Optimization, Inc. XPRESS-MP is a registered trademark of FICO Fair Isaac Corporation. Mosek is a registered trademark of Mosek ApS. Windows and Excel are registered trademarks of Microsoft Corporation. T X, L X, and -L X are trademarks of the American Mathematical Society. Lucida is a registered trademark of Bigelow & Holmes Inc. Acrobat is a registered trademark of Adobe Systems Inc. Other brands and their produ cts are trademarks of their respective holders. Information in this document is subject to change without no tice and does not represent a commitment on the part of Paragon Decision Technology B.V. The software de scribed in this document is furnished under a license agreement and may only be used and copied in accorda nce with the terms of the agreement. The documentation may not, in whole or in part, be copied, photoc opied, reproduced, translated, or reduced to any electronic medium or machine-readable form without pri or consent, in writing, from Paragon Decision Technology B.V. Paragon Decision Technology B.V. makes no representation o r warranty with respect to the adequacy of this documentation or the programs which it describes for any particular purpose or with respect to its adequacy to produce any particular result. In no event shall Paragon Decision Technology B.V., its employees, its contractors or the authors of this docume ntation be liable for special, direct, indirect or consequential damages, losses, costs, charges, claims, demands, or claims for lost proﬁts, fees or expenses of any nature or kind. In addition to the foregoing, users should recognize that al l complex software systems and their doc- umentation contain errors and omissions. The authors, Para gon Decision Technology B.V. and its em- ployees, and its contractors shall not be responsible under any circumstances for providing information or corrections to errors and omissions discovered at any tim e in this book or the software it describes, whether or not they are aware of the errors or omissions. The a uthors, Paragon Decision Technology B.V. and its employees, and its contractors do not recommend the use of the software described in this book for applications in which errors or omissions could thr eaten life, injury or signiﬁcant loss. This documentation was typeset by Paragon Decision Technol ogy B.V. using L X and the Lucida font family.

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Chapter 18 Mixed Complementarity Problems This chapter This chapter discusses the special identiﬁer types and lang uage constructs that Aimms oﬀers to allow you to formulate mixed complementarity probl ems. Although mixed complementarity problems do not involve opt imization, they are speciﬁed through variables which are linked to constrai nts in terms of these variables, and thus fall into the common framework of a MATHEMATICAL PROGRAM Aimms also supports nonlinear optimization problems with additi onal complementarity constraints (also known as MPCC or MPEC pro blems) 18.1 Complementarity problems Complementar- ity problems Complementarity relations arise in a variety of engineerin g and economics ap- plications, most commonly to express an equilibrium of quan tities such as forces or prices. One standard application in engineering a rises in contact me- chanics, where complementarity expresses the fact that fri ction occurs only when two bodies are in contact. Other applications are found in structural mechanics, structural design, traﬃc equilibrium and optim al control. Economic models Interest among economists in solving complementarity prob lems is due in part to increased use of computational general equilibrium mode ls, where for in- stance complementarity is used to express Walras’ Law, and i n part to the equivalence of various games to complementarity problems. Nonlinear optimization Some generalizations of nonlinear programming, such as mul ti-level optimi- zation—in which auxiliary objectives are to be minimized—m ay be reformu- lated as problems with complementarity conditions. Also, b y formulating the Kuhn-Tucker conditions of a nonlinear optimization model o ne obtains a com- plementarity problem, which could be solved by a complement arity solver. In the latter case, however, one requires second-order deriva tive information of all constraints in the original optimization model.

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Chapter 18. Mixed Complementarity Problems 258 Complementar- ity conditions Complementarity problems are, in general, systems of nonli near constraints where variables in the system are linked to constraints in th e form of comple- mentarity conditions. There are two forms of complementari ty conditions, the classical complementarity condition, and its generalizat ion, the mixed comple- mentarity condition. Classical com- plementarity conditions The classical form of a complementarity condition involves a nonnegative vari- able and an associated function (x) . It requires that 0 and (x) or 0 and (x) This condition states that of both inequalities, at least on e must reach its bound. Alternatively, one can formulate this complementar ity condition as 0, (x) 0 and (x) 0. Mixed comple- mentarity condition The mixed form of a complementarity condition involves a bou nded variable with an associated function (x) . It requires that and (x) or and (x) or and (x) This condition states that either must reach one of its bounds, or the func- tion (x) must be zero. A mixed complementarity condition can be split into two classical complementarity conditions (albeit by intro ducing auxiliary vari- ables). The classical complementarity condition, on the ot her hand, is a special case of the mixed complementarity condition by choosing 0 and = Special cases By choosing = and = , the mixed complementarity condition re- duces to the special case is “free” and (x) Another special case is obtained when . The mixed complementarity condition then reduces to and (x) is “free Supported com- plementarity conditions in Aimms All complementarity conditions described above can be repr esented by asso- ciating a variable with a single constraint, which will form the basis for repre- senting complementarity conditions in Aimms . Consider the inequalities and (x) where (x) is a nonlinear expression, and exactly two of the constants and are ﬁnite. The six possible cases are enumerated in Table 18.1 and are discussed below.

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Chapter 18. Mixed Complementarity Problems 259 Case ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite ﬁnite Table 18.1: Six allowed cases with exactly two ﬁnite bounds Case 1 The case and (x) corresponds to the mixed complementarity condition already discussed above: and (x) or and (x) or and (x) Case 2 The case and (x) corresponds to the classical com- plementarity condition 0 and (x) or 0 and (x) where and (x) (x) Case 3 The case and (x) corresponds to the classical complementarity condition 0 and (x) or 0 and (x) where and (x) (x) Case 4 The case and (x) corresponds to the classical complementarity condition 0 and (x) or 0 and (x) where and (x) (x)

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Chapter 18. Mixed Complementarity Problems 260 Case 5 The case and (x) corresponds to the classical complementarity condition 0 and (x) or 0 and (x) where and (x) (x) Case 6: The case and (x) with corresponds to the ﬁrst special case of the mixed complementarity condition is “free” and (x) where f(x) (x) Case 6: After the introduction of variables ,x 0 and functions (x) (x) (x) (x) (x) the case and (x) with corresponds to a system of three simultaneous complementarity conditions is “free” and (x) 0 and (x) or 0 and (x) 0 and (x) or 0 and (x) Aimms support Aimms supports the variable-constraint couples with two ﬁnite bo unds, as dis- cussed above, through the special COMPLEMENTARITY VARIABLE data type. The declaration and attributes of this data type are discussed i n the next section, while section 18.3 describes the declaration of mixed complementarity models through the common MATHEMATICAL PROGRAM declaration. Well-behaved systems Like with nonlinear optimization models, not all mixed comp lementarity sys- tems that can be formulated are well-behaved. For instance, a variable with an associated constraint 1 0, only admits the solutions 0 and 1, which would destroy the continuous character of complement arity problems. For systems of complementarity conditions that are not well -behaved, the so- lution process may produce no, or unexpected results.

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Chapter 18. Mixed Complementarity Problems 261 18.2 COMPLEMENTARITY VARIABLE declaration and attributes Complementar- ity variables To support you in formulating a complementarity model, Aimms provides a special type of variable, the COMPLEMENTARITY VARIABLE . The attributes of a com- plementarity variable allow you to declare an (indexed) cla ss of variables in a complementarity model along with their associated constra ints. The attributes of a COMPLEMENTARITY VARIABLE are listed in Table 18.2 Automatic sanity checks By construction, this new variable type automatically ensu res that every vari- able in a complementarity model is associated with a single c onstraint. Also, when Aimms detects that the total number of (ﬁnite) bounds on both the co m- plementarity variable and its associated constraint is not equal to two (as re- quired above), a compilation error will result. Thus, COMPLEMENTARITY VARIABLES will help to reduce the most common declaration errors for th is type of model. Attribute Value-type See also page INDEX DOMAIN index-domain 42 208 208 RANGE range 208 UNIT unit-valued expression 45 211 TEXT string 19 45 COMMENT comment string 19 COMPLEMENT expression 217 NONVAR STATUS reference 212 PROPERTY NoSave Complement Table 18.2: COMPLEMENTARITY VARIABLE attributes The INDEX DOMAIN attribute Through the INDEX DOMAIN attribute of a complementarity variable you can specify domain of tuples for which you want Aimms to generate a variable and its associated constraint. During generation, Aimms will only generate a vari- able for all tuples that satisfy all domain restrictions tha t you have imposed on the domain. The RANGE attribute In the RANGE attribute you can specify the lower and upper bound of a com- plementarity variable, in a similar manner for ordinary VARIABLES (see also Sec- tion 14.1 ). During generation, Aimms will perform a runtime check, for every individual tuple in the index domain, whether the number of nite bounds speciﬁed here, plus the number of ﬁnite bounds in the constra int speciﬁed in the COMPLEMENT attribute, exactly equals two.

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Chapter 18. Mixed Complementarity Problems 262 The COMPLEMENT attribute The COMPLEMENT attribute allows you to specify the constraint that must be a s- sociated with the complementarity variable at hand. With f(x,...) a general nonlinear function, the following types of expressions are allowed f(x,...) (variable must have a single-sided RANGE ), f(x,...) (variable must have a single-sided RANGE ), f(x,...) (variable must be free), f(x,...) (variable must be free), or f(x,...) (variable must be bounded). In addition, the COMPLEMENT attribute can hold a reference to an existing CON- STRAINT in your model, which then should hold a deﬁnition as one of the cases above. The COMPLEMENT attribute can also hold a scalar element parameter into the set AllConstraints , which oﬀers the possibility to assign diﬀerent con- straints to the complementarity variable in sequential sol ves. Constraint listing In the constraint listing, the constraints associated with a complementarity variable will be listed with a generated name consisting of t he name of the COMPLEMENTARITY VARIABLE with an additional suﬃx complement ”. The NONVAR STATUS attribute With the NONVAR STATUS attribute you can indicate for which tuples you want Aimms to consider the complementarity variable as a parameter, i. e. with the lower and upper bound set equal to the level value prior to sol ving the model (see also Section 14.1.1 ). From the mixed complementarity condition it follows that the function in the corresponding constraint is then al lowed to assume arbitrary values, whence there is no strict need to generate the variable and constraint for the solver. Positive and negative values The value of the NONVAR STATUS attribute must be an expression in some or all of the indices in the index list of the variable, allowing you to change the non- variable status of individual elements or groups of element s at once. When the NONVAR status assumes a positive value, Aimms will not generate the variable and its associated constraint. For negative values, the var iable and constraint will be generated, but reduces to the second special case of t he mixed comple- mentarity condition 0 and (x) is “free i.e. the function in the constraint will be allowed to assume arbitrary values. The UNIT attribute Providing a UNIT for a complementarity variable will help you in a number of ways. Aimms will help you to check the consistency of all the constraints and assignments in your model (including the expression in the COMPLEMENT attribute), and Aimms will use the units to scale the model that is sent to the solver

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Chapter 18. Mixed Complementarity Problems 263 Proper scaling of a model will generally result in a more accu rate and robust solution process. You can ﬁnd more information on the deﬁnit ion and use of units to scale mathematical programs in Chapter 31 The PROPERTY attribute Complementarity variables support the properties NoSave and Complement . With the property NoSave you indicate that you do not want to store data associated with this variable in a case. The Complement property indicates that you are in- terested in the level values of the constraint deﬁned in the COMPLEMENT attribute. When this property is set, Aimms will make the level value of this constraint available through the .Complement suﬃx of the complementarity variable at hand. Example The declaration of the complementarity variable MembraneHeight expresses a complementarity condition for the height of a membrane in a r ectangular (x,y) -grid, with a uniform external force acting on each cell in th e grid. COMPLEMENTARY VARIABLE: identifier : MembraneHeight index domain : (x,y) range : [MembraneLowerBound(x,y), MembraneUpperBound(x ,y)] complement : 4*MembraneHeight(x,y) - MembraneHeight(x+1,y) - MembraneHeight(x-1,y) - MembraneHeight(x,y+1) - MembraneHeight(x,y-1) - CellForce ; The complementarity condition expresses that either the me mbrane reaches one its given bounds (for instance, an obstacle placed in the way of the mem- brane), or the external force on the cell must be equal to the i nternal forces acting on the cell caused by diﬀerences in height with neighb oring cells. 18.3 Declaration of mixed complementarity models Mixed comple- mentarity models To deﬁne a pure mixed complementarity model, you must declar e a MATHEMA- TICAL PROGRAM (see also Section 15.1 ) and specify mcp as the TYPE attribute of the MATHEMATICAL PROGRAM . In the VARIABLES attribute you can specify a subset of the set of all COMPLEMENTARITY VARIABLES to be included in the mixed comple- mentarity model at hand. Based on this speciﬁcation, Aimms will automatically generate all constraints associated with these complement arity variables, re- sulting in a square system. Additional variables and constraints In addition, Aimms allows you to add ordinary variables to the VARIABLES at- tribute, and to specify additional constraints in the CONSTRAINTS attribute of the MATHEMATICAL PROGRAM that must be satisﬁed as well. If the solver used to solve the mixed complementarity model requires a square sys tem, Aimms will automatically add auxiliary constraints or variables to th e generated system,

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Chapter 18. Mixed Complementarity Problems 264 and provide the linkages with the ordinary variables and con straints you have added to the system. No optimization For a mixed complementarity problem you should not specify t he OBJECTIVE and DIRECTION attributes, as a complementarity solver will only compute a fea- sible solution that satisﬁes all the complementarity condi tions speciﬁed. If these attributes are not empty, Aimms will produce a runtime error when you apply the SOLVE statement the corresponding MATHEMATICAL PROGRAM (see also Section 15.3 ). Example A mixed complementarity model containing the declaration o f the comple- mentarity variable MembraneHeight declared in the previous section, is deﬁned by the following declaration. MATHEMATICAL PROGRAM: identifier : Membrane variables : AllVariables type : mcp ; As usual, you can solve the Membrane through the statement solve Membrane; which will generate the mixed complementarity model and inv oke a suitable solver for mcp problem type. 18.4 Declaration of MPCC models MPCC models Through the knitro solver, Aimms also supports m athematical p rograms with omplementarity c onstraints (MPCC models). MPCC models are also more com- monly denoted by other modeling languages as MPEC models, wh ich form a more general, and much more diﬃcult, class of optimization p roblems. A MPCC model is an ordinary NLP model with additional compleme ntarity con- straints that have to be satisﬁed. Declaring MPCC models To deﬁne a MPCC model, you must declare a MATHEMATICAL PROGRAM (see also Section 15.1 ) and specify mpcc as the TYPE attribute of the MATHEMATICAL PROGRAM The variable set of a MATHEMATICAL PROGRAM of a MPCC model can contain ordi- nary variables as well as complementarity variables. Contr ary to pure mixed complementary models, a MPCC model has an objective functio n.

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Chapter 18. Mixed Complementarity Problems 265 Solving MPCC models To solve MPCC models in Aimms , you need a license for the knitro solver. If you do not have a license for the knitro solver, Aimms will return an error that it has no suitable solver available for the mpcc class, whenever you try to solve a MPCC model. The knitro solver can also be used for solving pure mixed complementarity problems, but is, in general, far les s eﬃcient in that case than dedicated mcp solvers.

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