关于正方形阵列的边长最小化问题（BLMP）
On the Border Length Minimization Problem (BLMP) on a Square Array
摘要：
蛋白质/肽微阵列在癌症诊断中正迅速获得动力。高密度和高通量肽阵列被广泛用于检测肿瘤生物标志物、检查激酶活性、鉴定具有低血清滴度的抗体和定位抗体特征。提高微阵列制造的产量涉及解决一个称为边界长度最小化问题（BLMP）的硬组合优化问题。过去七年来，一个重要的问题仍然悬而未决，那就是《基本法》是否容易处理。通过证明BLMP是NP困难的，我们解决了这个问题。我们还提出了一个分层细化算法，可以细化任何启发式解决方案的BLMP问题。我们还证明了TSP+1-线程启发式算法是一个O（N）-近似。分层细化求解器可在http://launchpad.net/blm-solve上作为开源代码获得。
英文摘要：
Protein/Peptide microarrays are rapidly gaining momentum in the diagnosis of cancer. High-density and highthroughput peptide arrays are being extensively used to detect tumor biomarkers, examine kinase activity, identify antibodies having low serum titers and locate antibody signatures. Improving the yield of microarray fabrication involves solving a hard combinatorial optimization problem called the Border Length Minimization Problem (BLMP). An important question that remained open for the past seven years is if the BLMP is tractable or not. We settle this open problem by proving that the BLMP is NP-hard. We also present a hierarchical refinement algorithm which can refine any heuristic solution for the BLMP problem. We also prove that the TSP+1-threading heuristic is an O(N)- approximation. The hierarchical refinement solver is available as an opensource code at http://launchpad.net/blm-solve.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
