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<div class="title">A Brief History </div> </div>
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<div class="textblock"><p>DSDP began as a specialized solver for combinatorial optimization problems. Over the years, improvements in efficiency and design have enabled its use in many applications. Below is a brief history of DSDP.</p>
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<li>[1997] At the University of Iowa the authors release the initial version of DSDP. It solved the semidefinite relaxations of the maximum cut, minimum bisection, s-t cut, and bound constrained quadratic problems.</li>
<li>[1999] DSDP version 2 increased functionality to address semidefinite cones with rank-one constraint matrices and LP constraints. It was used specifically for combinatorial problems such as graph coloring, stable sets, and satisfiability problems.</li>
<li>[2000] DSDP version 3 was a preliminary implementation of a general purpose SDP solver that addressed applications from the Seventh DIMACS Implementation Challenge on Semidefinite and Related Optimization Problems. DSDP 3 also featured the initial release of PDSDP, the first parallel solver for semidefinite programming.</li>
<li>[2002] DSDP version 4 added new sparse data structures and linked to BLAS and LAPACK to improve efficiency and precision. A Lanczos based line search and efficient iterative solver were added. It solved all problems in the SDPLIB collection that includes examples from control theory, truss topology design, and relaxations of combinatorial problems.</li>
<li>[2004] DSDP version 5 features a new efficient interface for semidefinite constraints, and extensibility to structured applications in conic programming. Existence of the central path was ensured by bounding the variables. New applications from in computational chemistry, global optimization, and sensor network location motivated the improvements in efficiency in robustness. </li>
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