simple_cvxpy_solver

Instructions

This tool provides a more straightforward interface for CVXPY problems,
without requiring the full CVXPYProblem model structure.

Args:
    variables: List of variable definitions, each 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 description of the problem

Returns:
    A list of TextContent containing the solution or an error message

Implementation Reference

• usolver_mcp/server/main.py:465-564 (handler)

  Handler function for the 'simple_cvxpy_solver' tool. Constructs CVXPYProblem from simple inputs and delegates to solve_cvxpy_problem solver.

  @app.tool("simple_cvxpy_solver")
  async def simple_cvxpy_solver(
      variables: list[dict[str, Any]],
      objective_type: str,
      objective_expr: str,
      constraints: list[str],
      parameters: dict[str, Any] | None = None,
      description: str = "",
  ) -> list[TextContent]:
      """A simpler interface for solving CVXPY optimization problems.
  
      This tool provides a more straightforward interface for CVXPY problems,
      without requiring the full CVXPYProblem model structure.
  
      Args:
          variables: List of variable definitions, each 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 description of the problem
  
      Returns:
          A list of TextContent containing the solution or an error message
      """
      try:
          # Convert to Problem model
          problem_variables = []
          for var in variables:
              if "name" not in var or "shape" not in var:
                  return [
                      TextContent(
                          type="text",
                          text="Each variable must have 'name' and 'shape' fields",
                      )
                  ]
  
              problem_variables.append(CVXPYVariable(**var))
  
          try:
              obj_type = ObjectiveType(objective_type)
          except ValueError:
              return [
                  TextContent(
                      type="text",
                      text=(
                          f"Invalid objective type: {objective_type}. "
                          f"Must be one of: {', '.join([t.value for t in ObjectiveType])}"
                      ),
                  )
              ]
  
          objective = CVXPYObjective(type=obj_type, expression=objective_expr)
          problem_constraints = [CVXPYConstraint(expression=expr) for expr in constraints]
  
          problem = CVXPYProblem(
              variables=problem_variables,
              objective=objective,
              constraints=problem_constraints,
              parameters=parameters or {},
              description=description,
          )
  
          # Solve the problem
          result = solve_cvxpy_problem(problem)
  
          match result:
              case Success(solution):
                  return [
                      TextContent(
                          type="text",
                          text=json.dumps(
                              {
                                  "values": {
                                      k: v.tolist() if hasattr(v, "tolist") else v
                                      for k, v in solution.values.items()
                                  },
                                  "objective_value": solution.objective_value,
                                  "status": solution.status,
                                  "dual_values": {
                                      k: v.tolist() if hasattr(v, "tolist") else v
                                      for k, v in (solution.dual_values or {}).items()
                                  },
                              }
                          ),
                      )
                  ]
              case Failure(error):
                  return [
                      TextContent(type="text", text=f"Error solving problem: {error}")
                  ]
              case _:
                  return [
                      TextContent(
                          type="text",
                          text="Unexpected error in simple_cvxpy_solver",
                      )
                  ]
      except Exception as e:
          return [TextContent(type="text", text=f"Error in simple_cvxpy_solver: {e!s}")]

• usolver_mcp/models/cvxpy_models.py:7-52 (schema)

  Pydantic models defining input schema (CVXPYProblem, variables, objective, constraints) and output (CVXPYSolution) used internally by the tool.

  class ObjectiveType(str, Enum):
      """Enum for objective types in CVXPY."""
  
      MINIMIZE = "minimize"
      MAXIMIZE = "maximize"
  
  
  class CVXPYVariable(BaseModel):
      """Model representing a CVXPY variable."""
  
      name: str
      shape: int | tuple[int, ...]  # Can be scalar (int) or vector/matrix (tuple)
  
  
  class CVXPYConstraint(BaseModel):
      """Model representing a CVXPY constraint."""
  
      expression: str  # String representation of the constraint
      description: str = ""
  
  
  class CVXPYObjective(BaseModel):
      """Model representing a CVXPY objective."""
  
      type: ObjectiveType
      expression: str  # String representation of the objective function
  
  
  class CVXPYProblem(BaseModel):
      """Model representing a complete CVXPY optimization problem."""
  
      variables: list[CVXPYVariable]
      objective: CVXPYObjective
      constraints: list[CVXPYConstraint]
      parameters: dict[str, Any] = {}  # For A, b, etc.
      description: str = ""
  
  
  class CVXPYSolution(BaseModel):
      """Model representing a solution to a CVXPY problem."""
  
      values: dict[str, Any]  # Variable values
      objective_value: float | None
      status: str
      dual_values: dict[int, Any] | None = None  # Constraint index to dual value

• usolver_mcp/solvers/cvxpy_solver.py:55-110 (helper)

  Core solver logic: parses string expressions into CVXPY objects using safe eval, builds and solves the optimization problem with cvxpy, extracts primal/dual solutions.

  def solve_cvxpy_problem(problem: CVXPYProblem) -> Result[CVXPYSolution, str]:
      """Solve a CVXPY optimization problem.
  
      Args:
          problem: The problem definition
  
      Returns:
          Result containing a CVXPYSolution or an error message
      """
      try:
          # Create variables
          variables: dict[str, cp.Variable] = {}
          for var in problem.variables:
              variables[var.name] = create_variable(var.name, var.shape)
  
          # Parse objective
          objective_expr = parse_expression(
              problem.objective.expression, variables, problem.parameters
          )
          objective = (
              cp.Minimize(objective_expr)
              if problem.objective.type == ObjectiveType.MINIMIZE
              else cp.Maximize(objective_expr)
          )
  
          # Parse constraints
          constraints = []
          for _i, constraint in enumerate(problem.constraints):
              constraint_expr = parse_expression(
                  constraint.expression, variables, problem.parameters
              )
              constraints.append(constraint_expr)
  
          # Create and solve the problem
          prob = cp.Problem(objective, constraints)
          result = prob.solve()
  
          # Extract solution
          values = {name: var.value for name, var in variables.items()}
          dual_values = {
              i: constraint.dual_value
              for i, constraint in enumerate(constraints)
              if hasattr(constraint, "dual_value") and constraint.dual_value is not None
          }
  
          return Success(
              CVXPYSolution(
                  values=values,
                  objective_value=float(result) if result is not None else None,
                  status=prob.status,
                  dual_values=dual_values if dual_values else None,
              )
          )
      except Exception as e:
          return Failure(f"Error solving CVXPY problem: {e!s}")