Optimizer

Multi-Criteria Optimization

Security Level 1 (Enabled)

SindByte tool execution flow: voice command, AI parse, execute, validate, result

Description

Finds optimal solutions for mathematical optimization problems with multiple objectives and constraints. Supports linear and nonlinear optimization, minimization and maximization, as well as complex objective functions with restrictions.

JSON Parameters

{
  "objective": "minimize",
  "function": "(x-3)^2 + (y+2)^2",
  "variables": ["x", "y"],
  "bounds": {
    "x": [-10, 10],
    "y": [-10, 10]
  },
  "constraints": ["x + y <= 5"],
  "algorithm": "nelder-mead",
  "tolerance": 1e-6,
  "maxIterations": 10000
}

Parameter	Type	Required	Description
`objective`	String	Yes	"minimize" or "maximize"
`function`	String	Yes	Objective function as a formula
`variables`	Array	Yes	List of variable names
`bounds`	Object	No	Bounds for each variable [min, max]
`constraints`	Array	No	Constraints as inequalities
`algorithm`	String	No	nelder-mead, gradient, genetic
`tolerance`	Number	No	Convergence tolerance

Example Usage

API call:

POST /api/v1/tools/optimizer
Content-Type: application/json

{
  "objective": "minimize",
  "function": "(x-3)^2 + (y+2)^2",
  "variables": ["x", "y"],
  "bounds": {"x": [-10, 10], "y": [-10, 10]}
}

Examples

Simple quadratic minimization:

{
  "objective": "minimize",
  "function": "x^2",
  "variables": ["x"]
}

Rosenbrock function:

{
  "objective": "minimize",
  "function": "(1-x)^2 + 100*(y-x^2)^2",
  "variables": ["x", "y"],
  "bounds": {"x": [-5, 5], "y": [-5, 5]}
}

With constraints:

{
  "objective": "maximize",
  "function": "x*y",
  "variables": ["x", "y"],
  "constraints": ["x + y <= 10", "x >= 0"]
}

Return Values

{
  "optimalValue": 0.0,
  "solution": {
    "x": 3.0,
    "y": -2.0
  },
  "iterations": 127,
  "converged": true,
  "finalTolerance": 8.5e-7,
  "success": true
}

Field	Type	Description
`optimalValue`	Number	Optimal objective function value
`solution`	Object	Optimal variable values
`iterations`	Number	Iterations used
`converged`	Boolean	Convergence reached
`finalTolerance`	Number	Actual tolerance
`success`	Boolean	True if successful

Related Tools

Why This Tool Stands Out

Multiple Algorithms Choose the best approach for your problem - from gradients to genetic algorithms!
Constraints True constrained optimization - your solutions stay within the allowed region!
Fast Convergence Optimized algorithms find the optimum in just a few iterations!
Flexible From 1 to 1000 variables - scale your optimization problems!

Perfect for:

Engineers Design optimization
Quants Portfolio optimization
ML Experts Hyperparameter tuning

Time Savings

No measured time-saving or performance figures are available here. Results depend on the task and environment.

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