Use a deterministic numerical solver, not a black-box chat answer. Enter readable math syntax and get reproducible solution reports with tables, plots, and statistics for equations, ODEs, regression, optimization, and more.
Try instantly as guest, or register free to save work and remove guest limits. Open the solver instantly as a guest. No account required for a quick trial. Register free to save your work, reopen files, and remove guest limits. Affordable paid plans from $15/year. No programming required.
Solution Report
| Variable | Value Profile | ||||
|---|---|---|---|---|---|
| Name | Initial | Minimal | Maximal | Final | |
| 1 | A | 1 | 0.04979 | 1 | 0.049787 |
| 2 | B | 0 | 0 | 0.2497 | 0.047308 |
| 3 | C | 0 | 0 | 0.9029 | 0.902905 |
PolymathPlus gives students, instructors, and engineers a structured solving workflow: enter the model, run a deterministic solver, then inspect the generated report.
Solver Examples |
![]() |
Explore these examples to learn the simple problem entry syntax. Each example popup shows the problem input in the left panel and the solution output in the right panel.
Pricing |
![]() |
Our free license is powerful and available at no cost. Get started with it by simply registering and logging in. For advanced users, affordable paid licenses with expanded capabilities are also available, starting at just $15 annually.
Best for trying PolymathPlus and solving smaller coursework problems online.
Best for students who need higher limits and the Windows desktop package.
Best for researchers and professionals who need maximum capacity.
The following table lists some of the program key capacities and features per license.
| Capacity & Features | Free | Std | Pro |
| Online storage capacity | 5 | 100 | ∞ |
| Nonlinear equations | 5 | 10 | 100 |
| Differential equations | 5 | 10 | 2000 |
| Supporting auxiliary variables | 5 | 50 | 2000 |
| Max data-table rows | 15 | 100 | ∞ |
| Regression model variables | 2 | 10 | ∞ |
| ODE solution data points | 50 | 200 | 1000 |
| Max Opt. Iterations | 100 | 500 | 1200 |
| Max Opt. Model Variables | 2 | 4 | 30 |
| Solver requests per month | 100 | 1000 | ∞ |
| Online solver | ✅ | ✅ | ✅ |
| Windows desktop application | ✅ | ✅ | |
| Dynamic charts | ✅ | ✅ | |
| Advanced tools | ✅ | ||
| Annual Cost excluding sales tax (USD) | Free | $15 🛒 | $50 🛒 |
Users can purchase individual Student or Pro licenses through their online profile page.
| Site License | Annual Cost | |
| $370 | per 50 users | |
| $450 | per 100 users | |
| $1250 | per 50 users | |
| $1500 | per 100 users | |
Institutions with site licenses may install the desktop version on their public computers.
To initiate the registration of a new site, please reach out to us and provide the following details:
| Detail | Example | |
| 1. | Site name | Arizona State University |
| 2. | IT contact email | john123@asu.edu |
| 3. | Backup email | abra2759@asu.edu |
| 4. | Email extension/s | @asu.edu, @student.asu.edu |
| 5. | Site Address | ABC College 123 University Avenue College Town, CA 98765 United States |
Support and FAQ |
![]() |
Our support team typically replies to account, licensing, and common troubleshooting requests within one business day. For technical questions, enhancement requests, or bug reports, email admin@polymathplus.org with a brief description and any screenshots; for licensing or purchase inquiries, please include the account email and any relevant site details.
Below you'll find common support questions and answers:
PolymathPlus is math-solving software designed for students, scientists, and engineers, built to handle various numerical problems such as nonlinear equations, ordinary differential equations, linear/polynomial/nonlinear curve-fitting regression, and more.
At its core, it is a deterministic computational engine that applies well-established, rigorously validated numerical algorithms to solve mathematical and engineering models. For the same model and inputs, it produces reproducible results, which is essential for verification, traceability, and design confidence. This is fundamentally different from heuristic AI systems, which can generate variable outputs and may not always provide the consistency or numerical reliability required for engineering problem-solving.
Our goal is to deliver a user-friendly yet powerful math-solving tool that remains accessible and affordable for users around the world. With PolymathPlus, you can enter a math problem in plain text and instantly receive a numeric solution accompanied by a comprehensive report—all with a single click.
PolymathPlus offers a free, feature-rich online package.
PolymathPlus also offers affordable premium packages designed to handle more advanced tasks. These plans can solve larger and more complex problems, including cases involving more than five simultaneous nonlinear equations or ordinary differential equations
You can try the solver immediately as a guest — no account needed. A free registration is required to access all solver packages without guest limits.
The free package is designed for individual users. Organizations are asked to register their site and purchase a site license for their users.
Yes — PolymathPlus works on virtually any modern device.
The online version works through all major browsers—including Chrome, Safari, Opera, Firefox, and Edge—on both Windows and macOS laptops and desktops. You can also access PolymathPlus on Android and iOS smartphones or tablets using the supported web browsers.
For users with paid licenses, we also offer a dedicated Windows desktop version.
The legacy Polymath 6.x software is no longer supported, as the organization behind it has been dissolved. PolymathPlus was developed by a new, independent organization, maintaining full backward compatibility with the syntax of Polymath 6.1. At the same time, it delivers major advancements in algorithm optimization, capabilities, and underlying technology.
PolymathPlus is available online as a web application in both free and paid versions. The paid desktop version is offered with affordable Std and Pro licenses for individual users.
You can open the solver as a guest for a quick trial. Guest sessions have a brief wait between solves and do not support saving work. Register for a free account to save programs and reduce the wait time.
Click Login to register. After free sign-up, your password will be emailed to you, giving you access to the Free-tier solver.
PolymathPlus provides three types of individual user licenses:
Free
Std and
Pro.
The Std and Pro licenses are priced at 15 USD and 50 USD per year respectively.
PolymathPlus also offers discounted site licenses for organizations.
Click here for more details about the capabilities of each license and the available site-license options.
PolymathPlus uses well-established, finely tuned numerical solver algorithms.
These algorithms have been rigorously tested over more than 20 years of solving real-world engineering problems.
The core development team of PolymathPlus also created the earlier Polymath application, which was widely utilized as a numerical solver package within the global community of Chemical Engineers.
The development was also encouraged by:
|
CACHE | The Computer Aids for Chemical Engineering Education Corporation, as part of the |
![]() |
AIChE | The American Institute of Chemical Engineers |
After the dissolution of the Polymath team/organization, the principal developer took the initiative to rekindle the project. Drawing from more than 25 years of software development and extensive research in numerical packages, a new package was crafted.
Yes — a Windows desktop version is available for Std and Pro individual licenses, which can be downloaded from the user profile page. However, the free license does not include the desktop package. Sites with an active site license can install the desktop version on their public lab computers, but users covered by the site license can only use the desktop version on those public computers.
At present, our focus remains on the online and Windows desktop platforms, and there aren't immediate plans for a desktop version compatible with Mac or Linux. Nonetheless, this direction could be influenced by user demand and available resources.
The online version works on all common platforms, including Windows, Mac, Linux, iOS, and Android.
The user profile page shows your preferences and current license status. It also lets you upgrade your license by purchasing an individual or site license.
To claim a site license, you will need an access key, which is provided exclusively to members of organizations that have purchased a bulk site license.
The Site Admin page is designed for IT administrators who manage a site license. Access requires a dedicated access key.
The page provides guidance on configuring the site license and tracking license distribution and usage.
To register a new site, please ✉ contact us with the following information:
We will then provide an access key. The Site Admin IT contact can use this key to access the Site Admin page, where they can set up invoices, manage licenses, and follow deployment instructions.
This enables granting or licenses to site users for online access or on-site desktop use.
First, go to the PolymathPlus website and log in (or register if you haven’t already).
Then:

Note: The site-license key is intended only for authorized members of your organization.
The desktop installer MSI package can be downloaded from the profile page once on STD or PRO licenses. Please note these important points when installing the desktop application:

When you use Tools > Login in the desktop application, you may encounter the error: "Object reference not set to an instance of an object", and the application crashes.
This is a known bug in the older desktop release version SetupPolymathPlus_7051.msi.
To resolve this issue, please download and install the latest version from your profile page.
If you still wish to use an older version, please follow these steps to bypass the issue and complete the registration:
The Windows desktop application relies on the Microsoft Edge WebView2 Runtime. If Edge is not already installed on your computer, you will need to install the WebView2 Runtime separately.
If the application errors with 'The system cannot find the file specified (0x80070002)', download and install the WebView2 Runtime from the Microsoft link below:
WebView2 Evergreen Bootstrapper
This error occurs when the solver's adaptive step-size control reduces the step size excessively, preventing the equation set from converging. This results in the solver taking too many steps without reaching a solution.
To resolve this, try the following steps:
This error occurs when arithmetic operations produce numbers that are too large or too small, resulting in Infinity or NaN. Check your initial and final values for scale issues, and pay particular attention to power operations, which can produce extremely large numbers.
By purchasing the software, you are supporting its sustainability and ongoing development.
We are also actively seeking sponsors. PolymathPlus benefits universities, organizations, and their communities — and sponsors receive recognition for supporting a widely-used tool.
Organizations interested in sponsoring our activities can ✉ contact us directly.
Equations Solver |
![]() |
When solving nonlinear equations—whether a single equation or a system—write each equation so it equals 0 (root-finding form) and provide an initial guess for each variable. In other words, express each equation as 𝑓(𝑥)=0 and supply an initial guess for every nonlinear variable.
Here's a quick example showing how to set up and enter the data for solving two simultaneous nonlinear equations:
|
x2 + y = 12 2x + log(y+2) = 5 |
The unknown variables are x and y.
Below are the root expressions, each corresponding to one of the unknowns, along with initial estimates—both set to 1.
| • | Any part of a line starting with # is treated as a user comment. It's ignored during evaluation and has no effect on the calculations. |
| • | With implicit multiplication syntax, you may omit the multiplication operator * in some cases. For example, 2x is equivalent to 2*x. |
You can also define auxiliary expressions to help simplify long formulas. In the example below, we define a as an auxiliary expression—meaning it's treated as a separate, explicit variable that other expressions can reference. You don't need to provide initial guesses for auxiliary explicit variables—they're automatically computed from the nonlinear variables with provided initial guesses.
When solving a single nonlinear equation—even if you include as many explicit helper variables and expressions as you like—the way you set the starting values is a bit different. Instead of giving just one guess, you need to provide both a minimum and a maximum estimate for the variable.
Take a look at this example where we solve a nonlinear equation to find the value of V.
When solving multiple linear equations, you can enter them in the standard format—just as you'd normally write them. There's no need to rearrange the equations or provide any initial guesses. Simply ensure that all the equations are truly linear.
Here's an example of three linear equations being solved simultaneously:
|
a + 2c = 50 b = –2 + c a + 23c = 12 + b |
The following equations should be entered into PolymathPlus to solve the system described above:
Using implicit multiplication simplifies the syntax:
Differential Equations |
![]() |
PolymathPlus is designed to solve systems of simultaneous initial value problems (IVPs) involving first-order ordinary differential equations (ODEs).
Higher-order differential equations can be transformed into systems of first-order equations using standard mathematical techniques. Likewise, partial differential equations (PDEs) can be converted into ODEs for numerical solution using methods such as the method of lines.
Each equation in PolymathPlus must be written in first-order form, where the derivative of a dependent variable is isolated on the left-hand side and expressed as an algebraic function of independent and dependent variables. This structure ensures compatibility with the program's numerical solvers.
Consider this set of ordinary differential equations to be solved:
Given these initial and boundary conditions:
The equations can be entered in PolymathPlus using the following syntax:
You can also use either of the following two compatible syntax formats for the same problem:
The output shows a table of results along with an integration chart.

In addition to differential equations, PolymathPlus allows users to define explicit variables—algebraic expressions that depend on differential variables, the independent variable, and/or other explicit variables, provided no circular dependencies exist. These variables are computed directly from known values and can be used to simplify equations, define intermediate results, or support the overall structure of the model.
The example below illustrates a typical input format for solving a system of ordinary differential equations (ODEs) in PolymathPlus, including the use of explicit variables.
To enter this system of equations into PolymathPlus, you can use the following syntax:
When using implicit multipliers syntax, we can simplify the expressions to:
Curve Fitting |
![]() |
PolymathPlus supports curve fitting for linear, polynomial, multi-linear, and nonlinear regression. The report evaluates model variables, generates a regression chart and residual plots, and provides statistics on model accuracy.
Below is an example of the data entry required to solve a linear regression model for a given set of data points.
This example demonstrates a polynomial regression model of order 1 (i.e., linear regression), which finds the best-fitting linear equation for the given data points.

Below is an example of a data entry for solving nonlinear regression model for a given set of data points. The model variables to be found are a and b, for which we should also provide an initial guess.
The model eqaution to fit is as follows:
The data entry for this nonlinear regression model is as follows:
and the solution becomes:

PolymathPlus can calculate a definite integral from known discrete data points. The solver interpolates the supplied x-y data, builds a continuous curve from those points, and calculates the area under the curve over the requested interval. This is useful when the function is known through measurements, tabulated data, or simulation output rather than through a closed-form equation.
The default settings are designed to work well in almost all practical cases. Advanced users can still override the defaults by choosing the interpolation method and the number of sub-sections used for the numerical integration. Increasing the number of sub-sections can improve accuracy for rapidly changing curves, while simpler interpolation may be preferred for piecewise-linear data.
In this example, PolymathPlus integrates y(x) from x = 3 to x = 8.6. By default, it uses Akima interpolation with Simpson integration and 100 sub-sections.

Optional forms provide direct control when needed: use integrate y(x) 2 3 90 akm for Akima interpolation over 2-3 with 90 sub-sections, integrate y(x) 1 2 50 ccs for CCS interpolation over 1-2 with 50 sub-sections, or integrate y(x) 5 9 80 lin for linear interpolation over 5-9 with 80 sub-sections.
Constrained Optimization (NLP) |
![]() |
⚠ Experimental This NLP solver is a new feature, still in pilot and 🧪 validation mode.
PolymathPlus can find the minimum of a mathematical model expression while respecting user-defined constraints. This type of task is known as constrained nonlinear programming (NLP). The solver determines the variable values that best minimize the objective function, and the resulting report includes charts and statistics that show how well the model fits.
To set up an NLP problem in PolymathPlus, start by specifying the objective function you want to minimize using the min: keyword.
Constraints are added as expressions that must equal zero for equality constraints (use ... = 0),
or be greater than zero for inequality constraints (use ... > 0).
Write each constraint as a mathematical expression followed by the appropriate comparison.
Please Note: Be sure to rewrite each constraint so that all terms appear on one side of the equation or inequality, leaving zero on the other side.
A key requirement for the NLP solver is to provide bounds for each variable.
For every variable, you must enter an initial guess, a lower bound, and an upper bound
in the following format: variable | guess lower_bound upper_bound
This helps the solver search only within the meaningful range for your model.
Below is an example of how to enter data for a constrained optimization problem. In this example, the goal is to minimize a squared distance function, subject to nonlinear constraints that define the allowed region.
Mathematically, the problem is expressed as:
The corresponding data entry and the solution report are as follows:

Since the global minimum of the function is located at and this point satisfies all constraints (where and ), the solver converges to the optimal value of min F* = 0.
Least Squares (LSQ) optimization is a specific type of nonlinear programming (NLP) used to fit mathematical models to observed data. The main goal is to minimize the sum of squared differences (residuals) between the actual data points and the values predicted by the model. This approach is widely used in scientific modeling, as it provides a quantitative measure of how well the model matches the data under various equality and inequality constraints.
To perform LSQ optimization in PolymathPlus, you provide a set of observed data points and define a mathematical model that describes the relationship between variables. The solver then adjusts the model parameters to find the best fit, minimizing the total squared error.
Below is an example of how to enter data for a least squares optimization problem. In this example, the model uses a saturation kinetics form (which is commonly applied in enzyme kinetics and other scientific fields). The objective is to find the parameter values that make the model curve fit the data points as closely as possible.
The saturation kinetics form model:
The equation above represents a common model used to describe how a response variable y changes with respect to an independent variable x, where - we seek to find the best fitting model variables Vmax (represented by a) and Km (represented by b), by minimizing the residuals of calculated ycalc values compared to the observed y data points.
In the PolymathPlus program, we should define the data-table of the known variable. We should also define the model expression to optimize using the fit: keyword followed by the math expression.
The program will attempt to find the best suited model variables a and b, by minimizing the sum of squared residuals:
Here is the PolymathPlus program to solve the LSQ optimization problem described above:
and the solution becomes:

The verified solution for this dataset yields a ≈ 2.0 (representing Vmax) and b ≈ 0.5 (representing Km). By evaluating the residual plots provided in the report, users can confirm that the error is randomly distributed, indicating that the nonlinear model chosen is an appropriate fit for the data.
The program solution report includes charts to visualize the accuracy of the fit:

About Us |
![]() |
PolymathPlus began as a Windows desktop application and expanded its offerings to include an online web application. Our passion and dedication drive us to continually innovate and refine our solutions. We are committed to providing a top-notch numerical solver package to tackle mathematical challenges with ease and confidence.
![]() |
admin@polymathplus.org
sales@polymathplus.org |
![]() |
+1 530 405 9303 |
| +995 557 440 981 | |
| Pre-scheduled calls only 08:00-10:00 AM ET |
|
![]() |
Maintenance Status |
The development of our software had been encouraged by CACHE - The Computer Aids for Chemical Engineering Education Corporation, as part of the American Institute of Chemical Engineers.
Drawing from more than 25 years of software development and extensive research in numerical packages, we have crafted the new PolymathPlus package. We maintain ties with many US-based universities and colleges, as well as institutions across other regions worldwide.
Deliver the premier user-friendly, advanced math solver—accessible and affordable worldwide—the first choice for learners, professionals, and enthusiasts.
Research |
![]() |
Our research is dedicated to advancing numerical methods and optimization techniques to strengthen the capabilities of PolymathPlus. The following key research areas highlight their potential impact across disciplines commonly applied in science and engineering, particularly in design, modeling, and control.
Optimization solvers find a function's minimum or maximum.
Engineers often apply classical numerical methods like Gradient Descent, Conjugate Gradient, BFGS, and L-BFGS to solve these problems efficiently. These algorithms improve convergence and reduce computation time, especially for smooth, differentiable functions and large-scale systems.
To tackle Constrained optimization problems (where seeking the optimal maximum or minimum of a function while satisfying variable constraints), engineers use classical methods like the Augmented Lagrangian approach, Generalized Reduced Gradient (GRG), and Karush-Kuhn-Tucker (KKT) conditions. These techniques are designed to handle complex systems with multiple constraints, ensuring feasible and efficient solutions
Solving partial differential equations is central to modeling physical phenomena in engineering and science — from heat transfer and fluid dynamics to structural mechanics and electromagnetics. Robust numerical algorithms are developed to tackle these equations efficiently and accurately.
Common approaches include finite difference methods for structured grids, finite element methods for complex geometries, and spectral methods for high-precision solutions. These techniques enable engineers to simulate and analyze systems governed by PDEs across a wide range of applications.
The Simplex method solves linear programming problems by moving along the edges of feasible solutions to find the optimal one.
Enhancing Simplex algorithms with advanced pivot rules, degeneracy handling, and dual simplex implementations to handle large-scale linear optimization problems more efficiently. Research includes interior-point methods and revised simplex algorithms for improved numerical stability.
Developing specialized solvers for boundary value problems and parameter optimization in ordinary differential equations, including shooting methods and multiple shooting techniques.
Combining advanced regression techniques with ODE solvers for better model fitting in dynamic systems, enabling parameter estimation in complex mathematical models.
Chebyshev and Fourier transformations are mathematical tools used to represent functions using orthogonal basis sets. They enhance numerical solvers for discrete data, improving accuracy and computational efficiency in interpolation and approximation. These transformations also enable advanced solvers by replacing discrete data with continuous function representations.
These research efforts represent our ongoing commitment to improving computational mathematics. While we work to incorporate these advancements into PolymathPlus, we aim to provide users with reliable and efficient tools for solving complex mathematical problems.
Solution Reports |
![]() |
Below are sample reports generated by solving PolymathPlus programs. The input for each program is shown at the top of every report. Each group features example problems and their corresponding solution reports.
Start with the free online solver, then upgrade when you need larger problems, more capacity, or the Windows desktop package.
Terms and Conditions PolymathPlus does not warrant that the functions contained in the program will meet your requirements or that the operation of the program will be uninterrupted or error-free. In no event will PolymathPlus or its distributors or their dealers be liable to you for any damages, including any lost profit, lost savings, or other incidental or consequential damage. Please read the Terms of Service (TOS) below for further details.