Nonlinear　optimization　plays　an　important　role　in　science　computation　and　engineering　analysis.　Newton-like　method　is　popular　for　solving　the　nonlinear　optimization　problem.　an　inexact　Newton　algorithm　is　proposed　recently,　in　which　the　preconditioned　conjugate　gradient　method　is　applied　to　solve　the　Newton　equations.　Later,　the　algorithm　is　improved　by　efficiently　using　automatic　differentiation.　In　practical　application,　large-scale　systems　of　nonlinear　equations　typically　exhibit　either　sparsity　or　other　special　structures　in　their　Jacobian　matrices.　In　this　paper,　we　propose　the　structure　inexact　Newton　algorithm　(SINA),　The　algorithm　utilized　Semi-AD　techniques　can　improve　the　algorithm　efficiency　by　avoiding　the　unnecessary　computation.　Based　on　the　efficiency　coefficient　defined　by　Brent,　a　theoretical　efficiency　ratio　of　SINA　to　the　old　algorithm　is　introduced.　It　has　been　shown　that　SINA　is　much　more　efficient　than　the　old　one.　Furthermore,　this　theoretical　conclusion　is　supported　by　numerical　experiments.　©　2010　IEEE.