Solving Sparse Convex Quadratic Programming Problems with IMSL

Quadratic programming has a variety of applications, such as resource planning, portfolio optimization, and structural analysis. In addition, quadratic models are often used to formulate problems in pooling and blending, multiperiod tankage quality problems, problems involving separation sequences and many other areas.

This whitepaper contains a technical discussion of the sparse convex quadratic programming solver found in the IMSL C Numerical Library. The solver uses an infeasible primal-dual interior-point method to find an optimal solution, which is widely seen as a very efficient means to solve large-scale linear and convex quadratic programming problems fast and accurately.

This whitepaper also contains benchmarks for the sparse quadratic programming solver. Benchmarks for the linear programming solver are also available from Rogue Wave Software. If you are interested in receiving this benchmark - please indicate your interest below, in the comment field.

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