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We introduce an extended mathematical programming framework for specifying equilibrium problems and their variational representations, such as generalized Nash equilibrium, multiple optimization problems with equilibrium constraints, and (quasi-) variational inequalities, and computing solutions of them from modeling languages. We define a new set of constructs with which users annotate variables and equations of the model to describe equilibrium and variational problems. Our constructs enable a natural translation of the model from one formulation to another more computationally tractable form without requiring the modeler to supply derivatives. In the context of many independent agents in the equilibrium, we facilitate expression of sophisticated structures such as shared constraints and additional constraints on their solutions. We define a new concept, shared variables, and demonstrate its uses for sparse reformulation, equilibrium problems with equilibrium constraints, mixed pricing behavior of agents, and so on. We give some equilibrium and variational examples from the literature and describe how to formulate them using our framework. Experimental results comparing performance of various complementarity formulations for shared variables are given. Our framework has been implemented and is available within GAMS/EMP.

Typical formulations of the optimal power flow (OPF) problem rely on what is termed the "bus-branch" model, with network electrical behavior summarized in the Ybus admittance matrix. From a circuit perspective, this admittance representation restricts network elements to be voltage controlled and limitations of the Ybus have long been recognized. A fixed Ybus is unable to represent an ideal circuit breaker, and more subtle limitations appear in transformer modeling. In power systems parlance, more detailed approaches to overcome these limitations are termed "node-breaker" representations, but these are often cumbersome, and are not widely utilized in OPF. This paper develops a general network representation adapted to the needs of OPF, based on the Sparse Tableau Formulation (STF) with following advantages for OPF: (i) conceptual clarity in formulating constraints, allowing a comprehensive set of network electrical variables; (ii) improved fidelity in capturing physical behavior and engineering limits; (iii) added flexibility in optimization solution, in that elimination of intermediate variables is left to the optimization algorithm. The STF is then applied to OPF numerical case studies which demonstrate that the STF shows little or no penalty in computational speed compared to classic OPF representations, and sometimes provides considerable advantage in computational speed., Comment: 8 pages, 6 figures

Affine variational inequalities (AVI) are an important problem class that generalize systems of linear equations, linear complementarity problems and optimality conditions for quadratic programs. This paper describes PATHAVI, a structure-preserving pivotal approach, that can process (solve or determine infeasible) large-scale sparse instances of the problem efficiently, with theoretical guarantees and at high accuracy. PATHAVI implements a strategy that is known to process models with good theoretical properties without reducing the problem to specialized forms, since such reductions may destroy structure in the models and can lead to very long computational times. We demonstrate formally that PATHAVI implicitly follows the theoretically sound iteration paths, and can be implemented in a large scale setting using existing sparse linear algebra and linear programming techniques without employing a reduction. We also extend the class of problems that PATHAVI can process. The paper demonstrates the effectiveness of our approach by comparison to the PATH solver used on a complementarity reformulation of the AVI in the context of applications in friction contact and Nash Equilibria problems. PATHAVI is a general purpose solver, and freely available under the same conditions as PATH .