Solver for Excel

This add-in provides advanced solver functions for Python, including optimization algorithms. These functions leverage the widely used and powerful SciPy Python library. Since the functions are Python, they can be run using either Excel's integrated =PY() function or simply as a custom function which uses Pyodide locally in your browser. Either way you have full access to the Python source code for review and customization. More details are available in the Solver for Excel add-in documentation.

Features

🆓 Unlimited FREE use

🌐 Works in Excel for web and desktop

✅ Use Excel =PY() or local Python runtime

🔒 No data is shared outside Excel

🔍 Python source code available for review

📐 Advanced optimization algorithms

Functions

The following solver functions are included:

Local Optimization

• MINIMIZE: Multivariate minimization with gradient-based and derivative-free methods.
• MINIMIZE_SCALAR: Single-variable minimization using Brent, golden-section, or bounded methods.

Assignment Problems

• LINEAR_ASSIGNMENT: Solve linear assignment problems using the Hungarian algorithm.
• QUADRATIC_ASSIGNMENT: Solve quadratic assignment problems with interaction costs.

Global Optimization

• BASIN_HOPPING: Global minimization using basin-hopping with local search.
• DIFFERENTIAL_EVOLUTION: Population-based evolutionary global optimization.
• SHGO: Simplicial Homology Global Optimization for systematic global search.

Linear & Mixed-Integer Programming

• LINEAR_PROG: Solve linear programs with multiple solver backends.
• MILP: Solve mixed-integer linear programs using the HiGHS solver.

Root Finding

• ROOT: Solve nonlinear systems of equations with robust methods.
• ROOT_SCALAR: Scalar root-finding using Brent, Newton, secant, or other methods.
• FIXED_POINT: Find fixed points for scalar functions.

Background

Solvers address the fundamental mathematical challenge of **optimization** (finding the best solution).

Optimization finds the best solution from feasible alternatives. Linear Programming (LP) solves linear problems with guaranteed optimality. Mixed-Integer Programming (MILP) handles discrete decisions. Nonlinear Programming (NLP) solves curved problems. Global optimization finds the absolute best solution; Local optimization finds nearby minima efficiently. Root-finding solves nonlinear equations.

For more details, see the detailed documentation.

Use Cases

• Operations & Supply Chain: Route optimization minimizes transportation costs and delivery times. Production planning optimizes resource allocation and inventory levels. Workforce scheduling balances labor costs with service levels. Facility location selects optimal warehouse and distribution center locations. Vehicle routing determines efficient delivery sequences for fleets. Assignment problems solve crew scheduling and project resource allocation.
• Finance & Economics: Portfolio optimization balances risk and return by selecting optimal asset allocations. Pricing strategies determine revenue-maximizing prices under demand constraints. Capital budgeting allocates investment across competing projects. Asset-liability management matches financial obligations with available assets. Hedging strategies optimize financial instruments to reduce risk.
• Engineering & Design: Design optimization tunes parameters for structural strength, efficiency, or cost. Control system tuning optimizes PID controllers and feedback loops. Aerodynamic design optimizes airfoil shapes for lift and drag. Mechanical design optimizes component dimensions for weight and durability. Power systems optimization balances generation, transmission, and demand.
• Chemistry & Chemical Engineering: Reaction optimization tunes temperature, pressure, and catalyst to maximize product yield. Chemical equilibrium modeling predicts concentrations of reactants and products. Reaction pathway optimization identifies efficient synthetic routes. Process design optimizes separation, mixing, and heat transfer unit operations.
• Business Analytics & Optimization: Resource allocation maximizes productivity subject to budget and availability constraints. Scheduling problems optimize employee shifts, project timelines, and equipment utilization. Network design optimizes communication and transportation network topology. Inventory optimization balances holding costs against stockout risks. Revenue management optimizes pricing and capacity allocation to maximize profit.