mp.cost_table

class mp.cost_table

Bases: mp_table_subclass

mp.cost_table - Table for (polynomial and piecewise linear) cost parameters.

T = cost_table(poly_n, poly_coef, pwl_n, pwl_qty, pwl_cost);

Important

Since the dot syntax T.<var_name> is used to access table variables, you must use a functional syntax <method>(T,...), as opposed to the object-oriented T.<method>(...), to call standard mp.cost_table methods.

Standard table subscripting syntax is not available within methods of this class (references built-in subsref() and subsasgn() rather than the versions overridden by the table class). For this reason, some method implementations are delegated to static methods in mp.cost_table_utils where that syntax is available, making the code more readable.

mp.cost_table Methods:

• cost_table() - construct object

• poly_params() - create struct of polynomial parameters from mp.cost_table

• pwl_params() - create struct of piecewise linear parameters from mp.cost_table

• max_pwl_cost() - get maximum cost component used to specify pwl costs

An mp.cost_table has the following columns:

Name	Type	Description
poly_n	integer	\(n_\mathrm{poly}\), number of coefficients in polynomial cost curve, \(f_\mathrm{poly}(x) = c_0 + c_1 x... + c_N x^N\), where \(n_\mathrm{poly} = N + 1\)
poly_coef	double	matrix of coefficients \(c_j\), of polynomial cost \(f_\mathrm{poly}(x)\), where \(c_j\) is found in column \(j+1\)
pwl_n	double	\(n_\mathrm{pwl}\), number of data points \((x_1, f_1), (x_2, f_2), ..., (x_N, f_N)\) defining a piecewise linear cost curve, \(f_\mathrm{pwl}(x)\) where \(N = n_\mathrm{pwl}\)
pwl_qty	double	matrix of quantiy coordinates \(x_j\) for piecwise linear cost \(f_\mathrm{pwl}(x)\), where \(x_j\) is found in column \(j\)
pwl_cost	double	matrix of cost coordinates \(f_j\) for piecwise linear cost \(f_\mathrm{pwl}(x)\), where \(f_j\) is found in column \(j\)

See also mp.cost_table_utils, mp_table_subclass.

Constructor Summary

cost_table(varargin)

T = cost_table()
T = cost_table(poly_n, poly_coef, pwl_n, pwl_qty, pwl_cost)

For descriptions of the inputs, see the corresponding column in the class documentation above.

Inputs:

• poly_n (col vector of integers)

• poly_coef (matrix of doubles)

• pwl_n (col vector of integers)

• pwl_qty (matrix of doubles)

• pwl_cost (matrix of doubles)

Outputs:

T (mp.cost_table) – the cost table object

Method Summary

poly_params(idx, pu_base)

p = poly_params(obj, idx, pu_base)

Inputs:

• obj (mp.cost_table) – the cost table

• idx – (integer) : index vector of rows of interest, empty for all rows

• pu_base (double) – base used to scale quantities to per unit

Outputs:

p (struct) – polynomial cost parameters, struct with fields:

• have_quad_cost - true if any polynmial costs have order quadratic or less

• i0 - row indices for constant costs

• i1 - row indices for linear costs

• i2 - row indices for quadratic costs

• i3 - row indices for order 3 or higher costs

• k - constant term for all quadratic and lower order costs

• c - linear term for all quadratic and lower order costs

• Q - quadratic term for all quadratic and lower order costs

Implementation in mp.cost_table_utils.poly_params().

pwl_params(idx, pu_base, varargin)

p = pwl_params(obj, idx, pu_base)
p = pwl_params(obj, idx, pu_base, ng, dc)

Inputs:

• obj (mp.cost_table) – the cost table

• idx – (integer) : index vector of rows of interest, empty for all rows

• pu_base (double) – base used to scale quantities to per unit

• ng (integer) – number of units, default is # of rows in cost

• dc (boolean) – true if DC formulation (ng variables), otherwise AC formulation (2*ng variables), default is 1

Outputs:

p (struct) – piecewise linear cost parameters, struct with fields:

• n - number of piecewise linear costs

• i - row indices for piecewise linear costs

• A - constraint coefficient matrix for CCV formulation

• b - constraint RHS vector for CCV formulation

Implementation in mp.cost_table_utils.pwl_params().

max_pwl_cost()

maxc = max_pwl_cost(obj)

Input:

obj (mp.cost_table) – the cost table

Output:

maxc (double) – maximum cost component of all breakpoints used to specify piecewise linear costs

Implementation in mp.cost_table_utils.max_pwl_cost().

static poly_cost_fcn(xx, x_scale, ccm, idx)

f = mp.cost_table.poly_cost_fcn(xx, x_scale, ccm, idx)
[f, df] = mp.cost_table.poly_cost_fcn(...)
[f, df, d2f] = mp.cost_table.poly_cost_fcn(...)

Evaluates the sum of a set of polynomial cost functions \(f(x) = \sum_{i \in I}{f_i(x_i)}\), and optionally the gradient and Hessian.

Inputs:

• xx (single element cell array of double) – first element is a vector of the pre-scaled quantities \(x/\alpha\) used to compute the costs

• x_scale (double) – scalar \(\alpha\) used to scale the quantity value before evaluating the polynomial cost

• ccm (double) – cost coefficient matrix, element (i,j) is the coefficient of the (j-1) order term for cost i

• idx (integer) – index vector of subset \(I\) of rows of xx{1} and ccm of interest

Outputs:

• f (double) – value of cost function \(f(x)\)

• df (vector of double) – (optional) gradient of cost function

• d2f (matrix of double) – (optional) Hessian of cost function

static eval_poly_fcn(c, x)

f = mp.cost_table.eval_poly_fcn(c, x)

Evaluate a vector of polynomial functions, where …

f = c(:,1) + c(:,2) .* x + c(:,3) .* x^2 + ...

Inputs:

• c (matrix of double) – coefficient matrix, element (i,j) is the coefficient of the (j-1) order term for i-th element of f

• x (vector of double) – vector of input values

Outputs:

f (vector of double) – value of functions

static diff_poly_fcn(c)

c = mp.cost_table.diff_poly_fcn(c)

Compute the coefficient matrix for the derivatives of a set of polynomial functions from the coefficients of the functions.

Inputs:

c (matrix of double) – coefficient matrix for the functions, element (i,j) is the coefficient of the (j-1) order term of the i-th function

Outputs:

c (matrix of double) – coefficient matrix for the derivatives of the functions, element (i,j) is the coefficient of the (j-1) order term of the derivative of the i-th function