Constrained Optimization MCP Server

Solve convex optimization problems using CVXPY. This tool is ideal for mathematical optimization problems with convex objectives and constraints, including linear programming, quadratic programming, and semidefinite programming. Args: variables: List of variable definitions with 'name' and 'shape' objective_type: Either 'minimize' or 'maximize' objective_expr: The objective function expression as a string constraints: List of constraint expressions as strings parameters: Dictionary of parameter values (e.g., matrices A, b) description: Optional problem description Returns: Solution results including variable values and objective value Example: variables = [{"name": "x", "shape": 2}] objective_type = "minimize" objective_expr = "cp.sum_squares(x)" constraints = ["x >= 0", "cp.sum(x) == 1"]

{ "properties": { "constraints": { "items": { "type": "string" }, "title": "Constraints", "type": "array" }, "description": { "default": "", "title": "Description", "type": "string" }, "objective_expr": { "title": "Objective Expr", "type": "string" }, "objective_type": { "title": "Objective Type", "type": "string" }, "parameters": { "default": null, "title": "Parameters", "type": "object" }, "variables": { "items": { "type": "object" }, "title": "Variables", "type": "array" } }, "required": [ "variables", "objective_type", "objective_expr", "constraints" ], "type": "object" }