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optimization problem is convex, hence a found minimum (if iterations have converged) is guaranteed to be global." } ] } ], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Attributes", "Methods", "Returns", "Yields", "Receives", "Other Parameters", "Raises", "Warns", "Warnings", "Notes" ], "item_file": "/scipy/optimize/_lsq/lsq_linear.py", "item_line": 37, "item_type": "function", "aliases": [ "scipy.optimize.lsq_linear" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Text", "__tag": 4046, "value": "In this example, a problem with a large sparse arrays and bounds on the\nvariables is solved.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "import numpy as np\nfrom scipy.sparse import random_array\nfrom scipy.optimize import lsq_linear\nrng = np.random.default_rng()\nm = 2000\nn = 1000\nA = random_array((m, n), density=1e-4, random_state=rng)\nb = rng.standard_normal(m)\nlb = rng.standard_normal(n)\nub = lb + 1\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "res = lsq_linear(A, b, bounds=(lb, ub), lsmr_tol='auto', verbose=1)\n", "execution_status": "failure" } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "least_squares", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.optimize._lsq.least_squares:least_squares" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Nonlinear least squares with bounds on the variables." } ] } ], "type": null }, { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "nnls", "reference": { "__type": "RefInfo", "__tag": 4000, "module": "current-module", "version": "current-version", "kind": "to-resolve", "path": "nnls" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Linear least squares with non-negativity constraint." } ] } ], "type": null } ], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "A", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "b", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "bounds", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "(-inf, inf)" }, { 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[STIR] M. A. Branch, T. F. Coleman, and Y. Li, \"A Subspace, Interior,", " and Conjugate Gradient Method for Large-Scale Bound-Constrained", " Minimization Problems,\" SIAM Journal on Scientific Computing,", " Vol. 21, Number 1, pp 1-23, 1999.", ".. [BVLS] P. B. Start and R. L. Parker, \"Bounded-Variable Least-Squares:", " an Algorithm and Applications\", Computational Statistics, 10,", " 129-141, 1995." ], "qa": "scipy.optimize._lsq.lsq_linear:lsq_linear", "arbitrary": [], "local_refs": [ "A", "active_mask", "b", "bounds", "cost", "fun", "lsmr_maxiter", "lsmr_tol", "lsq_solver", "max_iter", "message", "method", "nit", "optimality", "status", "success", "tol", "unbounded_sol", "verbose", "x" ] }