Find a solution to a multivariable nonlinear equation F(x) = 0. You can also solve a scalar equation or linear system of equations, or a system represented by F(x) = G(x) in the problem-based approach (equivalent to F(x) – G(x) = 0 in the solver-based approach). For nonlinear systems, solvers convert the equation-solving problem to the optimization problem of minimizing the sum of squares of the components of F, namely min(∑Fi2(x)). Linear and scalar equations have different solution algorithms; see Equation Solving Algorithms.
Before you begin to solve an optimization problem, you must choose the appropriate approach: problem-based or solver-based. For details, see First Choose Problem-Based or Solver-Based Approach.
For the problem-based approach, create problem variables, and then
represent the equations in terms of these variables. For the problem-based
steps to take, see Problem-Based Workflow for Solving Equations. To
solve the resulting problem, use
For the solver-based steps to take, including defining the objective function and choosing the appropriate solver, see Solver-Based Optimization Problem Setup.
|Create equation problem|
|Evaluate optimization expression|
|Constraint violation at a point|
|Create empty optimization equality array|
|Create optimization variables|
|Display information about optimization object|
|Solve optimization problem or equation problem|
Solve a system of nonlinear equations using the problem-based approach.
Solve a polynomial system of equations using the problem-based approach.
Solve a sequence of problems using the previous solution as a start point.
Solve a system of nonlinear equations with constraints using the problem-based approach.
Use derivatives in nonlinear equation solving.
Solve a nonlinear system of equations without derivative information.
Solve a nonlinear system of equations with a known finite-difference sparsity pattern.
Learn techniques for solving nonlinear systems of equations with constraints.
Use multiple processors for optimization.
Perform gradient estimation in parallel.
Investigate factors for speeding optimizations.