Optimization

Generated Pydantic models for shared.optimization

pydantic model dakota.spec.shared.optimization.MethodConminCommonOptsMixin

Generated model for MethodConminCommonOptsMixin

Show JSON schema

{
   "title": "MethodConminCommonOptsMixin",
   "description": "Generated model for MethodConminCommonOptsMixin",
   "type": "object",
   "properties": {
      "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"
            }
         ]
      },
      "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"
            }
         ]
      },
      "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"
            }
         ]
      },
      "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"
            }
         ]
      },
      "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"
            }
         ]
      },
      "constraint_tolerance": {
         "default": 0.0,
         "description": "Maximum allowable constraint violation still considered feasible",
         "title": "Constraint Tolerance",
         "type": "number",
         "x-materialization": [
            {
               "ir_key": "method.constraint_tolerance",
               "ir_value_type": "Real",
               "storage_type": "DIRECT_VALUE"
            }
         ]
      }
   },
   "additionalProperties": false
}

Fields:

• constraint_tolerance ()

• convergence_tolerance ()

• max_function_evaluations ()

• max_iterations ()

• model_pointer ()

• scaling ()

• speculative ()

field constraint_tolerance: DakotaFloat = 0.0

Maximum allowable constraint violation still considered feasible

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 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 model_pointer: str | None = None

Identifier for model block to be used by a method

field scaling: Literal[True] | None = None

Turn on scaling for variables, responses, and constraints

field speculative: Literal[True] | None = None

Compute speculative gradients