optpp_q_newton
- pydantic model dakota.spec.method.optpp_q_newton.OptppQNewtonSelection
Generated model for OptppQNewtonSelection
Show JSON schema
{ "title": "OptppQNewtonSelection", "description": "Generated model for OptppQNewtonSelection", "type": "object", "properties": { "optpp_q_newton": { "$ref": "#/$defs/OptppQNewtonConfig", "x-materialization": [ { "ir_key": "method.algorithm", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "OPTPP_Q_NEWTON" } ] } }, "$defs": { "ArgaezTapia": { "additionalProperties": false, "description": "The merit function by Tapia and Argaez", "properties": { "argaez_tapia": { "const": true, "default": true, "description": "The merit function by Tapia and Argaez", "title": "Argaez Tapia", "type": "boolean", "x-materialization": [ { "enum_scope": "OPTPP", "ir_key": "method.optpp.merit_function", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ArgaezTapia" } ] } }, "title": "ArgaezTapia", "type": "object" }, "Debug": { "additionalProperties": false, "description": "Level 5 of 5 - maximum", "properties": { "debug": { "const": true, "default": true, "description": "Level 5 of 5 - maximum", "title": "Debug", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEBUG_OUTPUT" } ] } }, "title": "Debug", "type": "object" }, "ElBakry": { "additionalProperties": false, "description": "El-Bakry merit function", "properties": { "el_bakry": { "const": true, "default": true, "description": "El-Bakry merit function", "title": "El Bakry", "type": "boolean", "x-materialization": [ { "enum_scope": "OPTPP", "ir_key": "method.optpp.merit_function", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NormFmu" } ] } }, "title": "ElBakry", "type": "object" }, "GradientBasedLineSearch": { "additionalProperties": false, "description": "Set the search method to use the gradient", "properties": { "gradient_based_line_search": { "const": true, "default": true, "description": "Set the search method to use the gradient", "title": "Gradient Based Line Search", "type": "boolean", "x-materialization": [ { "ir_key": "method.optpp.search_method", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "gradient_based_line_search" } ] } }, "title": "GradientBasedLineSearch", "type": "object" }, "Normal": { "additionalProperties": false, "description": "Level 3 of 5 - default", "properties": { "normal": { "const": true, "default": true, "description": "Level 3 of 5 - default", "title": "Normal", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NORMAL_OUTPUT" } ] } }, "title": "Normal", "type": "object" }, "OptppQNewtonConfig": { "additionalProperties": false, "description": "Quasi-Newton optimization method", "properties": { "max_step": { "default": 1000.0, "description": "Max change in design point", "title": "Max Step", "type": "number", "x-materialization": [ { "ir_key": "method.optpp.max_step", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "gradient_tolerance": { "default": 0.0001, "description": "Stopping critiera based on L2 norm of gradient", "title": "Gradient Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.gradient_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "max_iterations": { "default": 9223372036854775807, "description": "Number of iterations allowed for optimizers and adaptive UQ methods", "minimum": 0, "title": "Max Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.max_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "convergence_tolerance": { "default": -1.7976931348623157e+308, "description": "Stopping criterion based on objective function or statistics convergence", "title": "Convergence Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.convergence_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" }, { "ir_key": "method.jega.percent_change", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "speculative": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Compute speculative gradients", "title": "Speculative", "x-materialization": [ { "ir_key": "method.speculative", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "max_function_evaluations": { "default": 9223372036854775807, "description": "Number of function evaluations allowed for optimizers", "minimum": 0, "title": "Max Function Evaluations", "type": "integer", "x-materialization": [ { "ir_key": "method.max_function_evaluations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "scaling": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Turn on scaling for variables, responses, and constraints", "title": "Scaling", "x-materialization": [ { "ir_key": "method.scaling", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "model_pointer": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Identifier for model block to be used by a method", "title": "Model Pointer", "x-block-pointer": "model", "x-materialization": [ { "ir_key": "method.model_pointer", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "search_method": { "anyOf": [ { "$ref": "#/$defs/ValueBasedLineSearch" }, { "$ref": "#/$defs/GradientBasedLineSearch" }, { "$ref": "#/$defs/SearchMethodTrustRegion" }, { "$ref": "#/$defs/TrPds" }, { "type": "null" } ], "default": null, "description": "Select a search method for Newton-based optimizers", "title": "Search Method", "x-union-pattern": 2 }, "merit_function": { "anyOf": [ { "$ref": "#/$defs/ElBakry" }, { "$ref": "#/$defs/ArgaezTapia" }, { "$ref": "#/$defs/VanShanno" } ], "description": "Balance goals of reducing objective function and satisfying constraints", "title": "Merit Function", "x-model-default": "ArgaezTapia", "x-union-pattern": 1 }, "steplength_to_boundary": { "default": -1.0, "description": "Controls how close to the boundary of the feasible region the algorithm is allowed to move", "title": "Steplength To Boundary", "type": "number", "x-materialization": [ { "ir_key": "method.optpp.steplength_to_boundary", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "centering_parameter": { "default": -1.0, "description": "Controls how closely the algorithm should follow the \"central path\"", "title": "Centering Parameter", "type": "number", "x-materialization": [ { "ir_key": "method.optpp.centering_parameter", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "id_method": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Name the method block; helpful when there are multiple", "title": "Id Method", "x-materialization": [ { "ir_key": "method.id", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "output": { "anyOf": [ { "$ref": "#/$defs/Debug" }, { "$ref": "#/$defs/Verbose" }, { "$ref": "#/$defs/Normal" }, { "$ref": "#/$defs/Quiet" }, { "$ref": "#/$defs/Silent" } ], "description": "Control how much method information is written to the screen and output file", "title": "Output", "x-model-default": "Normal", "x-union-pattern": 1 }, "final_solutions": { "default": 0, "description": "Number of designs returned as the best solutions", "minimum": 0, "title": "Final Solutions", "type": "integer", "x-materialization": [ { "ir_key": "method.final_solutions", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] } }, "title": "OptppQNewtonConfig", "type": "object" }, "Quiet": { "additionalProperties": false, "description": "Level 2 of 5 - less than normal", "properties": { "quiet": { "const": true, "default": true, "description": "Level 2 of 5 - less than normal", "title": "Quiet", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "QUIET_OUTPUT" } ] } }, "title": "Quiet", "type": "object" }, "SearchMethodTrustRegion": { "additionalProperties": false, "description": "Use trust region as the globalization strategy.", "properties": { "trust_region": { "const": true, "default": true, "description": "Use trust region as the globalization strategy.", "title": "Trust Region", "type": "boolean", "x-materialization": [ { "ir_key": "method.optpp.search_method", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "trust_region" } ] } }, "title": "SearchMethodTrustRegion", "type": "object" }, "Silent": { "additionalProperties": false, "description": "Level 1 of 5 - minimum", "properties": { "silent": { "const": true, "default": true, "description": "Level 1 of 5 - minimum", "title": "Silent", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SILENT_OUTPUT" } ] } }, "title": "Silent", "type": "object" }, "TrPds": { "additionalProperties": false, "description": "Use direct search as the local search in a trust region method", "properties": { "tr_pds": { "const": true, "default": true, "description": "Use direct search as the local search in a trust region method", "title": "Tr Pds", "type": "boolean", "x-materialization": [ { "ir_key": "method.optpp.search_method", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "tr_pds" } ] } }, "title": "TrPds", "type": "object" }, "ValueBasedLineSearch": { "additionalProperties": false, "description": "Use only function values for line search", "properties": { "value_based_line_search": { "const": true, "default": true, "description": "Use only function values for line search", "title": "Value Based Line Search", "type": "boolean", "x-materialization": [ { "ir_key": "method.optpp.search_method", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "value_based_line_search" } ] } }, "title": "ValueBasedLineSearch", "type": "object" }, "VanShanno": { "additionalProperties": false, "description": "The merit function by Vanderbei and Shanno", "properties": { "van_shanno": { "const": true, "default": true, "description": "The merit function by Vanderbei and Shanno", "title": "Van Shanno", "type": "boolean", "x-materialization": [ { "enum_scope": "OPTPP", "ir_key": "method.optpp.merit_function", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VanShanno" } ] } }, "title": "VanShanno", "type": "object" }, "Verbose": { "additionalProperties": false, "description": "Level 4 of 5 - more than normal", "properties": { "verbose": { "const": true, "default": true, "description": "Level 4 of 5 - more than normal", "title": "Verbose", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VERBOSE_OUTPUT" } ] } }, "title": "Verbose", "type": "object" } }, "additionalProperties": false, "required": [ "optpp_q_newton" ] }
- field optpp_q_newton: OptppQNewtonConfig [Required]
- classmethod get_registry() dict[str, type[MethodSelection]]
Get registry, performing deferred registration on first call
- classmethod get_union()
Generate Union from all registered selections
- pydantic model dakota.spec.method.optpp_q_newton.OptppQNewtonConfig
Quasi-Newton optimization method
Show JSON schema
{ "title": "OptppQNewtonConfig", "description": "Quasi-Newton optimization method", "type": "object", "properties": { "max_step": { "default": 1000.0, "description": "Max change in design point", "title": "Max Step", "type": "number", "x-materialization": [ { "ir_key": "method.optpp.max_step", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "gradient_tolerance": { "default": 0.0001, "description": "Stopping critiera based on L2 norm of gradient", "title": "Gradient Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.gradient_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "max_iterations": { "default": 9223372036854775807, "description": "Number of iterations allowed for optimizers and adaptive UQ methods", "minimum": 0, "title": "Max Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.max_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "convergence_tolerance": { "default": -1.7976931348623157e+308, "description": "Stopping criterion based on objective function or statistics convergence", "title": "Convergence Tolerance", "type": "number", "x-materialization": [ { "ir_key": "method.convergence_tolerance", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" }, { "ir_key": "method.jega.percent_change", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "speculative": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Compute speculative gradients", "title": "Speculative", "x-materialization": [ { "ir_key": "method.speculative", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "max_function_evaluations": { "default": 9223372036854775807, "description": "Number of function evaluations allowed for optimizers", "minimum": 0, "title": "Max Function Evaluations", "type": "integer", "x-materialization": [ { "ir_key": "method.max_function_evaluations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "scaling": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Turn on scaling for variables, responses, and constraints", "title": "Scaling", "x-materialization": [ { "ir_key": "method.scaling", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] }, "model_pointer": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Identifier for model block to be used by a method", "title": "Model Pointer", "x-block-pointer": "model", "x-materialization": [ { "ir_key": "method.model_pointer", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "search_method": { "anyOf": [ { "$ref": "#/$defs/ValueBasedLineSearch" }, { "$ref": "#/$defs/GradientBasedLineSearch" }, { "$ref": "#/$defs/SearchMethodTrustRegion" }, { "$ref": "#/$defs/TrPds" }, { "type": "null" } ], "default": null, "description": "Select a search method for Newton-based optimizers", "title": "Search Method", "x-union-pattern": 2 }, "merit_function": { "anyOf": [ { "$ref": "#/$defs/ElBakry" }, { "$ref": "#/$defs/ArgaezTapia" }, { "$ref": "#/$defs/VanShanno" } ], "description": "Balance goals of reducing objective function and satisfying constraints", "title": "Merit Function", "x-model-default": "ArgaezTapia", "x-union-pattern": 1 }, "steplength_to_boundary": { "default": -1.0, "description": "Controls how close to the boundary of the feasible region the algorithm is allowed to move", "title": "Steplength To Boundary", "type": "number", "x-materialization": [ { "ir_key": "method.optpp.steplength_to_boundary", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "centering_parameter": { "default": -1.0, "description": "Controls how closely the algorithm should follow the \"central path\"", "title": "Centering Parameter", "type": "number", "x-materialization": [ { "ir_key": "method.optpp.centering_parameter", "ir_value_type": "Real", "storage_type": "DIRECT_VALUE" } ] }, "id_method": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Name the method block; helpful when there are multiple", "title": "Id Method", "x-materialization": [ { "ir_key": "method.id", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "output": { "anyOf": [ { "$ref": "#/$defs/Debug" }, { "$ref": "#/$defs/Verbose" }, { "$ref": "#/$defs/Normal" }, { "$ref": "#/$defs/Quiet" }, { "$ref": "#/$defs/Silent" } ], "description": "Control how much method information is written to the screen and output file", "title": "Output", "x-model-default": "Normal", "x-union-pattern": 1 }, "final_solutions": { "default": 0, "description": "Number of designs returned as the best solutions", "minimum": 0, "title": "Final Solutions", "type": "integer", "x-materialization": [ { "ir_key": "method.final_solutions", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] } }, "$defs": { "ArgaezTapia": { "additionalProperties": false, "description": "The merit function by Tapia and Argaez", "properties": { "argaez_tapia": { "const": true, "default": true, "description": "The merit function by Tapia and Argaez", "title": "Argaez Tapia", "type": "boolean", "x-materialization": [ { "enum_scope": "OPTPP", "ir_key": "method.optpp.merit_function", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "ArgaezTapia" } ] } }, "title": "ArgaezTapia", "type": "object" }, "Debug": { "additionalProperties": false, "description": "Level 5 of 5 - maximum", "properties": { "debug": { "const": true, "default": true, "description": "Level 5 of 5 - maximum", "title": "Debug", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEBUG_OUTPUT" } ] } }, "title": "Debug", "type": "object" }, "ElBakry": { "additionalProperties": false, "description": "El-Bakry merit function", "properties": { "el_bakry": { "const": true, "default": true, "description": "El-Bakry merit function", "title": "El Bakry", "type": "boolean", "x-materialization": [ { "enum_scope": "OPTPP", "ir_key": "method.optpp.merit_function", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NormFmu" } ] } }, "title": "ElBakry", "type": "object" }, "GradientBasedLineSearch": { "additionalProperties": false, "description": "Set the search method to use the gradient", "properties": { "gradient_based_line_search": { "const": true, "default": true, "description": "Set the search method to use the gradient", "title": "Gradient Based Line Search", "type": "boolean", "x-materialization": [ { "ir_key": "method.optpp.search_method", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "gradient_based_line_search" } ] } }, "title": "GradientBasedLineSearch", "type": "object" }, "Normal": { "additionalProperties": false, "description": "Level 3 of 5 - default", "properties": { "normal": { "const": true, "default": true, "description": "Level 3 of 5 - default", "title": "Normal", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NORMAL_OUTPUT" } ] } }, "title": "Normal", "type": "object" }, "Quiet": { "additionalProperties": false, "description": "Level 2 of 5 - less than normal", "properties": { "quiet": { "const": true, "default": true, "description": "Level 2 of 5 - less than normal", "title": "Quiet", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "QUIET_OUTPUT" } ] } }, "title": "Quiet", "type": "object" }, "SearchMethodTrustRegion": { "additionalProperties": false, "description": "Use trust region as the globalization strategy.", "properties": { "trust_region": { "const": true, "default": true, "description": "Use trust region as the globalization strategy.", "title": "Trust Region", "type": "boolean", "x-materialization": [ { "ir_key": "method.optpp.search_method", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "trust_region" } ] } }, "title": "SearchMethodTrustRegion", "type": "object" }, "Silent": { "additionalProperties": false, "description": "Level 1 of 5 - minimum", "properties": { "silent": { "const": true, "default": true, "description": "Level 1 of 5 - minimum", "title": "Silent", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SILENT_OUTPUT" } ] } }, "title": "Silent", "type": "object" }, "TrPds": { "additionalProperties": false, "description": "Use direct search as the local search in a trust region method", "properties": { "tr_pds": { "const": true, "default": true, "description": "Use direct search as the local search in a trust region method", "title": "Tr Pds", "type": "boolean", "x-materialization": [ { "ir_key": "method.optpp.search_method", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "tr_pds" } ] } }, "title": "TrPds", "type": "object" }, "ValueBasedLineSearch": { "additionalProperties": false, "description": "Use only function values for line search", "properties": { "value_based_line_search": { "const": true, "default": true, "description": "Use only function values for line search", "title": "Value Based Line Search", "type": "boolean", "x-materialization": [ { "ir_key": "method.optpp.search_method", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "value_based_line_search" } ] } }, "title": "ValueBasedLineSearch", "type": "object" }, "VanShanno": { "additionalProperties": false, "description": "The merit function by Vanderbei and Shanno", "properties": { "van_shanno": { "const": true, "default": true, "description": "The merit function by Vanderbei and Shanno", "title": "Van Shanno", "type": "boolean", "x-materialization": [ { "enum_scope": "OPTPP", "ir_key": "method.optpp.merit_function", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VanShanno" } ] } }, "title": "VanShanno", "type": "object" }, "Verbose": { "additionalProperties": false, "description": "Level 4 of 5 - more than normal", "properties": { "verbose": { "const": true, "default": true, "description": "Level 4 of 5 - more than normal", "title": "Verbose", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VERBOSE_OUTPUT" } ] } }, "title": "Verbose", "type": "object" } }, "additionalProperties": false }
- Fields:
- field centering_parameter: DakotaFloat = -1.0
Controls how closely the algorithm should follow the “central path”
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- field convergence_tolerance: DakotaFloat = -1.7976931348623157e+308
Stopping criterion based on objective function or statistics convergence
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- field final_solutions: int = 0
Number of designs returned as the best solutions
- Constraints:
ge = 0
- field gradient_tolerance: DakotaFloat = 0.0001
Stopping critiera based on L2 norm of gradient
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- field id_method: str | None = None
Name the method block; helpful when there are multiple
- field max_function_evaluations: int = 9223372036854775807
Number of function evaluations allowed for optimizers
- Constraints:
ge = 0
- field max_iterations: int = 9223372036854775807
Number of iterations allowed for optimizers and adaptive UQ methods
- Constraints:
ge = 0
- field max_step: DakotaFloat = 1000.0
Max change in design point
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
- field merit_function: ElBakry | ArgaezTapia | VanShanno [Optional]
Balance goals of reducing objective function and satisfying constraints
- field model_pointer: str | None = None
Identifier for model block to be used by a method
- field output: Debug | Verbose | Normal | Quiet | Silent [Optional]
Control how much method information is written to the screen and output file
- field scaling: Literal[True] | None = None
Turn on scaling for variables, responses, and constraints
- field search_method: ValueBasedLineSearch | GradientBasedLineSearch | SearchMethodTrustRegion | TrPds | None = None
Select a search method for Newton-based optimizers
- field speculative: Literal[True] | None = None
Compute speculative gradients
- field steplength_to_boundary: DakotaFloat = -1.0
Controls how close to the boundary of the feasible region the algorithm is allowed to move
- Constraints:
func = <function _serialize_dakota_float at 0x7f2a3de76700>
return_type = float | str
when_used = json
Generated Pydantic models for method.optpp_q_newton
