Sage Reference Manual: Numerical Optimization - Mirrors

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Sage Reference Manual: Numerical Optimization, Release 6.1.1 get_min(v) Returns the minimum value of a variable. INPUT: •v – a variable (not a MIPVariable, but one of its elements). OUTPUT: Minimum value of the variable, or None if the variable has no lower bound. EXAMPLE: sage: p = MixedIntegerLinearProgram() sage: v = p.new_variable() sage: p.set_objective(v[1]) sage: p.get_min(v[1]) 0.0 sage: p.set_min(v[1],6) sage: p.get_min(v[1]) 6.0 sage: p.set_min(v[1], None) sage: p.get_min(v[1]) get_values(*lists) Return values found by the previous call to solve(). INPUT: •Any instance of MIPVariable (or one of its elements), or lists of them. OUTPUT: •Each instance of MIPVariable is replaced by a dictionary containing the numerical values found for each corresponding variable in the instance. •Each element of an instance of a MIPVariable is replaced by its corresponding numerical value. Note: While a variable may be declared as binary or integer, its value as returned by the solver is of type float. EXAMPLE: sage: p = MixedIntegerLinearProgram() sage: x = p.new_variable() sage: y = p.new_variable(dim=2) sage: p.set_objective(x[3] + 3*y[2][9] + x[5]) sage: p.add_constraint(x[3] + y[2][9] + 2*x[5], max=2) sage: p.solve() 6.0 To return the optimal value of y[2][9]: sage: p.get_values(y[2][9]) 2.0 To get a dictionary identical to x containing optimal values for the corresponding variables sage: x_sol = p.get_values(x) sage: x_sol.keys() [3, 5] 18 Chapter 2. Mixed integer linear programming
Sage Reference Manual: Numerical Optimization, Release 6.1.1 Obviously, it also works with variables of higher dimension: sage: y_sol = p.get_values(y) We could also have tried sage: [x_sol, y_sol] = p.get_values(x, y) Or: sage: [x_sol, y_sol] = p.get_values([x, y]) is_binary(e) Tests whether the variable e is binary. Variables are real by default. INPUT: •e – A variable (not a MIPVariable, but one of its elements.) OUTPUT: True if the variable e is binary; False otherwise. EXAMPLE: sage: p = MixedIntegerLinearProgram() sage: v = p.new_variable() sage: p.set_objective(v[1]) sage: p.is_binary(v[1]) False sage: p.set_binary(v[1]) sage: p.is_binary(v[1]) True is_integer(e) Tests whether the variable is an integer. Variables are real by default. INPUT: •e – A variable (not a MIPVariable, but one of its elements.) OUTPUT: True if the variable e is an integer; False otherwise. EXAMPLE: sage: p = MixedIntegerLinearProgram() sage: v = p.new_variable() sage: p.set_objective(v[1]) sage: p.is_integer(v[1]) False sage: p.set_integer(v[1]) sage: p.is_integer(v[1]) True is_real(e) Tests whether the variable is real. Variables are real by default. INPUT: •e – A variable (not a MIPVariable, but one of its elements.) OUTPUT: 2.2. Classes and methods 19
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CHAPTER SIX INDICES AND TABLES •
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BIBLIOGRAPHY [HPS08] J. Hoffstein,
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PYTHON MODULE INDEX n sage.numerica
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INDEX A add_col() (sage.numerical.b
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