{"id":{"repo_id":"lethbridge","oai_identifier":"oai:opus.uleth.ca:10133/3216"},"canonical_url":"https://search.dev.ndltd.org/etd/lethbridge/oai:opus.uleth.ca:10133/3216","repository":{"repo_id":"lethbridge","name":"University of Lethbridge","base_url":"https://opus.uleth.ca/server/oai/request"},"display":{"title":"DSJM : a software toolkit for direct determination of sparse Jacobian matrices","abstract":"DSJM is a software toolkit written in portable C++ that enables direct determination of sparse Jacobian matrices whose sparsity pattern is a priori known. Using the seed matrix S 2 Rn×p, the Jacobian A 2 Rm×n can be determined by solving AS = B, where B 2 Rm×p has been obtained via finite difference approximation or forward automatic differentiation. Seed matrix S is defined by the nonzero unknowns in A. DSJM includes well-known as well as new column ordering heuristics. Numerical testing is highly promising both in terms of running time and the number of matrix-vector products needed to determine A.","abstract_html":"DSJM is a software toolkit written in portable C++ that enables direct determination of sparse Jacobian matrices whose sparsity pattern is a priori known. Using the seed matrix S 2 Rn×p, the Jacobian A 2 Rm×n can be determined by solving AS = B, where B 2 Rm×p has been obtained via finite difference approximation or forward automatic differentiation. Seed matrix S is defined by the nonzero unknowns in A. DSJM includes well-known as well as new column ordering heuristics. Numerical testing is highly promising both in terms of running time and the number of matrix-vector products needed to determine A.","abstract_has_math":false,"creators":["Hasan, Mahmudul"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-27T20:02:47Z","subjects":["Sparse matrices","Sparse matrices -- Computer programs","Jacobians -- Data processing","Dissertations, Academic"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/3216"],"render_values":[{"text":"hdl:10133/3216","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2011"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Sparse matrices","Sparse matrices -- Computer programs","Jacobians -- Data processing","Dissertations, Academic"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/3216"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["DSJM is a software toolkit written in portable C++ that enables direct determination of sparse Jacobian matrices whose sparsity pattern is a priori known. Using the seed matrix S 2 Rn×p, the Jacobian A 2 Rm×n can be determined by solving AS = B, where B 2 Rm×p has been obtained via finite difference approximation or forward automatic differentiation. Seed matrix S is defined by the nonzero unknowns in A. DSJM includes well-known as well as new column ordering heuristics. Numerical testing is highly promising both in terms of running time and the number of matrix-vector products needed to determine A."]},{"key":"dc:title","label":"Title","values":["DSJM : a software toolkit for direct determination of sparse Jacobian matrices"]}]}],"canonical_facts":{"dc:date.issued":["2011"],"dc:description.other":["DSJM is a software toolkit written in portable C++ that enables direct determination of sparse Jacobian matrices whose sparsity pattern is a priori known. Using the seed matrix S 2 Rn×p, the Jacobian A 2 Rm×n can be determined by solving AS = B, where B 2 Rm×p has been obtained via finite difference approximation or forward automatic differentiation. Seed matrix S is defined by the nonzero unknowns in A. DSJM includes well-known as well as new column ordering heuristics. Numerical testing is highly promising both in terms of running time and the number of matrix-vector products needed to determine A."],"dc:identifier":["hdl:10133/3216"],"dc:subject":["Sparse matrices","Sparse matrices -- Computer programs","Jacobians -- Data processing","Dissertations, Academic"],"dc:title":["DSJM : a software toolkit for direct determination of sparse Jacobian matrices"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:02:47Z"}