Abstract
Semidefinite programming (SDP) is a branch of convex programming (CP) that has been a subject of intensive research since the early 1990’s [1]–[9]. The continued interest in SDP has been motivated mainly by two reasons. First, many important classes of optimization problems such as linear-programming (LP) and convex quadratic-programming (QP) problems can be viewed as SDP problems, and many CP problems of practical usefulness that are neither LP nor QP problems can also be formulated as SDP problems. Second, several interior-point methods that have proven efficient for LP and convex QP problems have been extended to SDP in recent years.
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(2007). Semidefinite and Second-Order Cone Programming. In: Practical Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-71107-2_14
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DOI: https://doi.org/10.1007/978-0-387-71107-2_14
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