# Building Expressions Faster¶

## Expression Generation¶

Pyomo expressions can be constructed using native binary operators in Python. For example, a sum can be created in a simple loop:

M = ConcreteModel()
M.x = Var(range(5))

s = 0
for i in range(5):
s = s + M.x[i]


Additionally, Pyomo expressions can be constructed using functions that iteratively apply Python binary operators. For example, the Python sum() function can be used to replace the previous loop:

s = sum(M.x[i] for i in range(5))


The sum() function is both more compact and more efficient. Using sum() avoids the creation of temporary variables, and the summation logic is executed in the Python interpreter while the loop is interpreted.

## Linear, Quadratic and General Nonlinear Expressions¶

Pyomo can express a very wide range of algebraic expressions, and there are three general classes of expressions that are recognized by Pyomo:

• linear polynomials
• nonlinear expressions, including higher-order polynomials and expressions with intrinsic functions

These classes of expressions are leveraged to efficiently generate compact representations of expressions, and to transform expression trees into standard forms used to interface with solvers. Note that There not all quadratic polynomials are recognized by Pyomo; in other words, some quadratic expressions are treated as nonlinear expressions.

For example, consider the following quadratic polynomial:

s = sum(M.x[i] for i in range(5))**2


This quadratic polynomial is treated as a nonlinear expression unless the expression is explicilty processed to identify quadratic terms. This lazy identification of of quadratic terms allows Pyomo to tailor the search for quadratic terms only when they are explicitly needed.

## Pyomo Utility Functions¶

Pyomo includes several similar functions that can be used to create expressions:

prod
A function to compute a product of Pyomo expressions.
quicksum
A function to efficiently compute a sum of Pyomo expressions.
sum_product
A function that computes a generalized dot product.

### prod¶

The prod function is analogous to the builtin sum() function. Its main argument is a variable length argument list, args, which represents expressions that are multiplied together. For example:

M = ConcreteModel()
M.x = Var(range(5))
M.z = Var()

# The product M.x[0] * M.x[1] * ... * M.x[4]
e1 = prod(M.x[i] for i in M.x)

# The product M.x[0]*M.z
e2 = prod([M.x[0], M.z])

# The product M.z*(M.x[0] + ... + M.x[4])
e3 = prod([sum(M.x[i] for i in M.x), M.z])


### quicksum¶

The behavior of the quicksum function is similar to the builtin sum() function, but this function often generates a more compact Pyomo expression. Its main argument is a variable length argument list, args, which represents expressions that are summed together. For example:

M = ConcreteModel()
M.x = Var(range(5))

# Summation using the Python sum() function
e1 = sum(M.x[i]**2 for i in M.x)

# Summation using the Pyomo quicksum function
e2 = quicksum(M.x[i]**2 for i in M.x)


The summation is customized based on the start and linear arguments. The start defines the initial value for summation, which defaults to zero. If start is a numeric value, then the linear argument determines how the sum is processed:

• If linear is False, then the terms in args are assumed to be nonlinear.
• If linear is True, then the terms in args are assumed to be linear.
• If linear is None, the first term in args is analyze to determine whether the terms are linear or nonlinear.

This argument allows the quicksum function to customize the expression representation used, and specifically a more compact representation is used for linear polynomials. The quicksum function can be slower than the builtin sum() function, but this compact representation can generate problem representations more quickly.

Consider the following example:

M = ConcreteModel()
M.A = RangeSet(100000)
M.p = Param(M.A, mutable=True, initialize=1)
M.x = Var(M.A)

start = time.time()
e = sum( (M.x[i] - 1)**M.p[i] for i in M.A)
print("sum:      %f" % (time.time() - start))

start = time.time()
generate_standard_repn(e)
print("repn:     %f" % (time.time() - start))

start = time.time()
e = quicksum( (M.x[i] - 1)**M.p[i] for i in M.A)
print("quicksum: %f" % (time.time() - start))

start = time.time()
generate_standard_repn(e)
print("repn:     %f" % (time.time() - start))



The sum consists of linear terms because the exponents are one. The following output illustrates that quicksum can identify this linear structure to generate expressions more quickly:

sum:      1.447861
repn:     0.870225
quicksum: 1.388344
repn:     0.864316


If start is not a numeric value, then the quicksum sets the initial value to start and executes a simple loop to sum the terms. This allows the sum to be stored in an object that is passed into the function (e.g. the linear context manager linear_expression).

Warning

By default, linear is None. While this allows for efficient expression generation in normal cases, there are circumstances where the inspection of the first term in args is misleading. Consider the following example:

M = ConcreteModel()
M.x = Var(range(5))

e = quicksum(M.x[i]**2 if i > 0 else M.x[i] for i in range(5))


The first term created by the generator is linear, but the subsequent terms are nonlinear. Pyomo gracefully transitions to a nonlinear sum, but in this case quicksum is doing additional work that is not useful.

### sum_product¶

The sum_product function supports a generalized dot product. The args argument contains one or more components that are used to create terms in the summation. If the args argument contains a single components, then its sequence of terms are summed together; the sum is equivalent to calling quicksum. If two or more components are provided, then the result is the summation of their terms multiplied together. For example:

M = ConcreteModel()
M.z = RangeSet(5)
M.x = Var(range(10))
M.y = Var(range(10))

# Sum the elements of x
e1 = sum_product(M.x)

# Sum the product of elements in x and y
e2 = sum_product(M.x, M.y)

# Sum the product of elements in x and y, over the index set z
e3 = sum_product(M.x, M.y, index=M.z)


The denom argument specifies components whose terms are in the denominator. For example:

# Sum the product of x_i/y_i
e1 = sum_product(M.x, denom=M.y)

# Sum the product of 1/(x_i*y_i)
e2 = sum_product(denom=(M.x, M.y))


The terms summed by this function are explicitly specified, so sum_product can identify whether the resulting expression is linear, quadratic or nonlinear. Consequently, this function is typically faster than simple loops, and it generates compact representations of expressions..

Finally, note that the dot_product function is an alias for sum_product.