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• ## doc/en/thematic_tutorials/linear_programming.rst

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# User Minh Van Nguyen <nguyenminh2@gmail.com>
# Date 1284192578 25200
# Node ID be78ff3f4cd1636513861d89d195922b278fdec8
#9836: reviewer patch: Linear Programming Thematic Tutorial

diff --git a/doc/en/thematic_tutorials/linear_programming.rst b/doc/en/thematic_tutorials/linear_programming.rst
 a (Mixed Integer) Linear Programming Linear Programming (Mixed Integer) ================================== This document explains the use of Linear Programming (LP) -- and of Mixed Integer Linear Programming (MILP) -- in Sage by illustrating it with several problems it can solve. Most of the examples given are motivated by graph-theoretic concerns, and should be understandabe without any specific knowledge of this field. As a tool in Combinatorics, using Linear Programming amounts to understanding how to reformulate an optimization (or existence) problem through linear constraints. This document explains the use of linear programming (LP) -- and of mixed integer linear programming (MILP) -- in Sage by illustrating it with several problems it can solve. Most of the examples given are motivated by graph-theoretic concerns, and should be understandable without any specific knowledge of this field. As a tool in Combinatorics, using linear programming amounts to understanding how to reformulate an optimization (or existence) problem through linear constraints. .. centered:: *(this is a translation of a chapter from the book "Calcul mathematique avec Sage")* This is a translation of a chapter from the book Calcul mathematique avec Sage _. Definition ---------- Set to bear once more upon our shoulders the heavy weight of mathematical formalism, let us give here the usual definition of what a Linear Program is : it is defined by a matrix A: \mathbb{R}^m\mapsto \mathbb{R}^n, along with two vectors b,c\in \mathbb{R}^n. Solving a Linear Program is a quest for a vector x maximizing an *objective* function and satisfying a set of constraints, i.e. Here we present the usual definition of what a linear program is: it is defined by a matrix A: \mathbb{R}^m \mapsto \mathbb{R}^n, along with two vectors b,c \in \mathbb{R}^n. Solving a linear program is searching for a vector x maximizing an *objective* function and satisfying a set of constraints, i.e. .. MATH:: c^t x = \max_{x' \text{ such that}Ax'\leq b}c^t x' c^t x = \max_{x' \text{ such that } Ax' \leq b} c^t x' (where the ordering u\leq u' between two vectors means that the entries of u' are pairwise greater than the entries of u). We also write : where the ordering u \leq u' between two vectors means that the entries of u' are pairwise greater than the entries of u. We also write: .. MATH:: \text{Max : }&c^t x\\ \text{ Such that : }&Ax\leq b \text{Max: } & c^t x\\ \text{Such that: } & Ax \leq b Equivalently, we can also say that solving a linear program amounts to maximizing a linear function defined over a polytope (preimage or A^{-1}(\leq b)). These definitions, however, do not tell us how to use linear programming in combinatorics. In the following, we will try to fix this by showing how to solve optimization problems like the Knapsack problem, the Maximum Matching problem, and a Flow problem. Equivalently, we can also say that solving a linear program amounts to maximizing a linear function defined over a polytope (preimage or A^{-1} (\leq b)). These definitions, however, do not tell us how to use linear programming in combinatorics. In the following, we will show how to solve optimization problems like the Knapsack problem, the Maximum Matching problem, and a Flow problem. Mixed Integer Linear Programming Mixed integer linear programming -------------------------------- There is a bad news coming along with this definition of linear programming : a LP can be solved in polynomial time. This is indeed a bad news, because this would mean than unless we define LP of exponential size, we can not expect LP to solve NP-complete problems, which would be a disappointment. On a brighter side, it becomes NP-Complete to solve a Linear Program  if we are allowed to specify constraints of a different kind : requiring that some variables be integers instead of real values. Such a LP is actually called a "Mixed Integer Linear Program" (some variables can be integers, some other reals). Hence, we can expect to find in the MILP framework a *wide* range of expressivity. There is a bad news coming along with this definition of linear programming: an LP can be solved in polynomial time. This is indeed a bad news, because this would mean that unless we define LP of exponential size, we can not expect LP to solve NP-complete problems, which would be a disappointment. On a brighter side, it becomes NP-complete to solve a linear program if we are allowed to specify constraints of a different kind: requiring that some variables be integers instead of real values. Such an LP is actually called a "mixed integer linear program" (some variables can be integers, some other reals). Hence, we can expect to find in the MILP framework a *wide* range of expressivity. Practically ----------- Practical --------- The MILP class ^^^^^^^^^^^^^^^^^^ The MILP class in Sage represents a ... MILP ! It is also used to solve regular LP. It has a very small number of methods, meant to define our set of constraints and variables, then to read the solution found by the solvers once computed. It is also possible to export a MILP defined with Sage to a .lp or .mps file, understood by most solvers. The MILP class in Sage represents a MILP! It is also used to solve regular LP. It has a very small number of methods, meant to define our set of constraints and variables, then to read the solution found by the solvers once computed. It is also possible to export a MILP defined with Sage to a .lp or .mps file, understood by most solvers. Let us ask Sage to solve the following LP : Let us ask Sage to solve the following LP: .. MATH:: \text{Max : }&x+y-3z\\ \text{Such that : }&x+2y \leq 4\\ \text{}&5z - y \leq 8\\ \text{Max: } & x + y - 3z\\ \text{Such that: } & x + 2y \leq 4\\ \text{} & 5z - y \leq 8\\ To achieve it, we need to define a corresponding MILP object, along with the 3 variables we need :: To achieve it, we need to define a corresponding MILP object, along with the 3 variables we need:: sage: p = MixedIntegerLinearProgram() sage: x, y, z = p['x'], p['y'], p['z'] sage: p = MixedIntegerLinearProgram() sage: x, y, z = p['x'], p['y'], p['z'] The objective function Next, we set the objective function .. link :: sage: p.set_objective( x + y + 3*z ) sage: p.set_objective(x + y + 3*z) And finally the constraints And finally we set the constraints .. link :: sage: p.add_constraint( x + 2*y <= 4 ) sage: p.add_constraint( 5*z - y  <= 8 ) The solve method returns by default the optimal value reached by the objective function sage: p.add_constraint(x + 2*y <= 4) sage: p.add_constraint(5*z - y <= 8) The solve method returns by default the optimal value reached by the objective function .. link :: sage: round(p.solve(),2) 8.8 sage: round(p.solve(), 2) 8.8 We can read the optimal assignation found by the solver for x, y and z through the get_values method We can read the optimal assignation found by the solver for x, y and z through the get_values method .. link :: sage: round(p.get_values(x),2) 4.0 sage: round(p.get_values(y), 2) 0.0 sage: round(p.get_values(z), 2) 1.6 sage: round(p.get_values(x), 2) 4.0 sage: round(p.get_values(y), 2) 0.0 sage: round(p.get_values(z), 2) 1.6 Variables ^^^^^^^^^ The variables associated with an instance of MILP belong to the MIPVariable class, though we should not be concerned with this. In the previous example, we obtained these variables through the "shortcut" p['x'], which is easy enough when our LP is defined over a small number of variables. This being said, the LP/MILP we will present afterwards very often require one to associate one -- or many -- variables to each member of a list of objects, which can be integers, or the vertices or edges of a graph, among plenty of other alternatives. This means we will very soon need to talk about vectors of variables, if not of dictionaries of variables. The variables associated with an instance of MILP belong to the MIPVariable class, though we should not be concerned with this. In the previous example, we obtained these variables through the "shortcut" p['x'], which is easy enough when our LP is defined over a small number of variables. This being said, the LP/MILP we will present afterwards very often require us to associate one -- or many -- variables to each member of a list of objects, which can be integers, or the vertices or edges of a graph, among plenty of other alternatives. This means we will very soon need to talk about vectors of variables or even dictionaries of variables. If a LP requires us to define variables named x_1, \dots, x_{15}, we will this time make use of the new_variable method If an LP requires us to define variables named x_1, \dots, x_{15}, we will this time make use of the new_variable method .. link :: sage: x = p.new_variable() sage: x = p.new_variable() It is now very easy to define constraints using our 15 variables :: sage: p.add_constraint( x[1] + x[12] - x[14] >= 8 ) sage: p.add_constraint(x[1] + x[12] - x[14] >= 8) Notice that we did not need to define the length of our vector. Actually, x would accept any immutable object as a key, as a dictionary would. We can now write Notice that we did not need to define the length of our vector. Actually, x would accept any immutable object as a key, as a dictionary would. We can now write .. link :: sage: p.add_constraint( x["I am a valid key"] ...                   + x[("a",pi)] <= 3 ) sage: p.add_constraint(x["I am a valid key"] ...                    + x[("a",pi)] <= 3) Other LP may require variables indexed several times. Of course, it is already possible to emulate it by using tuples like x[(2,3)], though to keep the code understandable the method new_variable accepts as a parameter the integer dim, which lets us define the dimension of the variable. We can now write Other LPs may require variables indexed several times. Of course, it is already possible to emulate it by using tuples like x[(2,3)], though to keep the code understandable the method new_variable accepts as a parameter the integer dim, which lets us define the dimension of the variable. We can now write .. link :: sage: y = p.new_variable(dim = 2) sage: p.add_constraint( y[3][2] + x[5] == 6) sage: y = p.new_variable(dim=2) sage: p.add_constraint(y[3][2] + x[5] == 6) Typed variables """"""""""""""" By default, all the LP variables are assumed to be non-negativereals. They can be defined as binary through the parameter binary = True (or integer with integer = True). Lower and upper bounds can be defined or re-defined (for instance when you want some variables to be negative) using the methods set_min and set_max. By default, all the LP variables are assumed to be non-negative reals. They can be defined as binary through the parameter binary=True (or integer with integer=True). Lower and upper bounds can be defined or re-defined (for instance when you want some variables to be negative) using the methods set_min and set_max. It is also possible to change the type of a variable after it has been created with the methods set_binary and set_integer. It is also possible to change the type of a variable after it has been created with the methods set_binary and set_integer. Basic Linear Programs Basic linear programs --------------------- Knapsack ^^^^^^^^ The *Knapsack* problem is the following : given a collection of items having both a weight and a *usefulness*, we would like to fill a bag whose capacity is constrained through maximizing the usefulness of the items it contains (we will here consider their sum). The *Knapsack* problem is the following: given a collection of items having both a weight and a *usefulness*, we would like to fill a bag whose capacity is constrained through maximizing the usefulness of the items it contains (we will here consider their sum). For the purpose of this tutorial, we set the restriction that the bag can only carry a certain total weight. To achieve this, we have to associate to each object o of our collection C a binary variable taken[o], set to 1 when the object is in the bag, and to 0 otherwise. We are trying to solve the following MILP To achieve this, we have to associate to each object o of our collection C a binary variable taken[o], set to 1 when the object is in the bag, and to 0 otherwise. We are trying to solve the following MILP .. MATH:: \text{Max : }&\sum_{o\in L}usefulness_o\times taken_o\\ \text{Tel que : }&\sum_{o\in L} poids_o\times taken_o \leq C\\ \text{Max: } & \sum_{o \in L} \text{usefulness}_o \times \text{taken}_o\\ \text{Such that: } & \sum_{o \in L} \text{usefulness}_o \times \text{taken}_o \leq C\\ Using \Sage, we will give to our items a random weight :: Using Sage, we will give to our items a random weight:: sage: C = 1 sage: C = 1 .. link :: sage: L = ["Casserole", "Livre", "Couteau", ...        "Gourde", "Lampe de poche"] sage: L = ["pan", "book", "knife", "gourd", "flashlight"] .. link :: sage: L.extend( ["divers_"+str(i) for i in range(20)] ) sage: L.extend(["divers_" + str(i) for i in range(20)]) .. link :: sage: poids = {} sage: usefulness = {} sage: poids = {} sage: usefulness = {} .. link :: sage: set_random_seed(685474) sage: for o in L: ...      poids[o]  = random() ...      usefulness[o] = random() sage: set_random_seed(685474) sage: for o in L: ...       poids[o] = random() ...       usefulness[o] = random() We can now define the MILP itself :: sage: p = MixedIntegerLinearProgram() sage: taken = p.new_variable( binary = True ) sage: p = MixedIntegerLinearProgram() sage: taken = p.new_variable(binary=True) .. link :: sage: p.add_constraint( ...     sum( poids[o] * taken[o] for o in L ) <= C ) sage: p.add_constraint(sum(poids[o] * taken[o] for o in L) <= C) .. link :: sage: p.set_objective( ...     sum( usefulness[o] * taken[o] for o in L ) ) sage: p.set_objective(sum(usefulness[o] * taken[o] for o in L)) .. link :: sage: p.solve() 3.1502766806530307 sage: taken = p.get_values(taken) sage: p.solve() 3.1502766806530307 sage: taken = p.get_values(taken) The solution found is (of course) admissible :: sage: sum( poids[o] * taken[o] for o in L ) 0.69649597966191712 sage: sum(poids[o] * taken[o] for o in L) 0.69649597966191712 Should we take a flashlight ? Should we take a flashlight? .. link :: sage: taken["Lampe de poche"] 1.0 sage: taken["flashlight"] 1.0 Wise advice. Based on purely random considerations. Matching -------- Given a graph G, a matching is a set of pairwise disjoint edges. The empty set being a matching, we naturally focus our attention on maximum matchings : we want to find in a graph a matching whose cardinality is maximal. Computing the maximum matching in a graph is a polynomial problem, which is a famous result of Edmonds : his algorithm is based on local improvements, and the proof that a given matching is maximum if it can not be improved. This algorithm is not the hardest to implement among those Graph Theory can offer, though this problem can be modeled with a very simple MILP. Given a graph G, a matching is a set of pairwise disjoint edges. The empty set is a trivial matching. So we focus our attention on maximum matchings: we want to find in a graph a matching whose cardinality is maximal. Computing the maximum matching in a graph is a polynomial problem, which is a famous result of Edmonds. Edmonds' algorithm is based on local improvements and the proof that a given matching is maximum if it cannot be improved. This algorithm is not the hardest to implement among those graph theory can offer, though this problem can be modeled with a very simple MILP. To do it, we need -- as previously -- to associate a binary variable to each one of our objects : the edges of our graph (a value of 1 meaning that the corresponding edge is included in the maximum matching). Our constraint on the edges taken being that they are disjoint, it is enough to require that, x and y being two edges and m_x,m_y their associated variables, the inequality m_x + m_y \leq 1 is satisfied, as we are sure that the two of them can not both belong to the matching. Hence, we are able to write the MILP we want. However, the number of inequalities can be easily decreaded by noticing that two edges can not be taken simultaneously inside a matching if and only if they have a common endpoint v. We can then require instead that at most one edge incident to v be taken inside the matching (which is a linear constraint). We will be solving : To do it, we need -- as previously -- to associate a binary variable to each one of our objects: the edges of our graph (a value of 1 meaning that the corresponding edge is included in the maximum matching). Our constraint on the edges taken being that they are disjoint, it is enough to require that, x and y being two edges and m_x, m_y their associated variables, the inequality m_x + m_y \leq 1 is satisfied, as we are sure that the two of them cannot both belong to the matching. Hence, we are able to write the MILP we want. However, the number of inequalities can be easily decreased by noticing that two edges cannot be taken simultaneously inside a matching if and only if they have a common endpoint v. We can then require instead that at most one edge incident to v be taken inside the matching, which is a linear constraint. We will be solving: .. MATH:: \text{Max : }&\sum_{e\in E(G)}m_e\\ \text{Tel que : }&\forall v, \sum_{e\in E(G)\atop v\sim e} m_e \leq 1\\ \text{Max: } & \sum_{e \in E(G)} m_e\\ \text{Such that: } & \forall v, \sum_{e \in E(G) \atop v \sim e} m_e \leq 1 Let us write the Sage code of this MILP:: sage: g = graphs.PetersenGraph() sage: p = MixedIntegerLinearProgram() sage: matching = p.new_variable(binary = True) sage: g = graphs.PetersenGraph() sage: p = MixedIntegerLinearProgram() sage: matching = p.new_variable(binary=True) .. link :: sage: p.set_objective(sum( matching[e] ...                   for e in g.edges(labels = False))) sage: p.set_objective(sum(matching[e] for e in g.edges(labels=False))) .. link :: sage: for v in g: ...      p.add_constraint(sum( matching[e] ...           for e in g.edges_incident(v, labels = False)) <= 1) sage: for v in g: ...       p.add_constraint(sum(matching[e] ...           for e in g.edges_incident(v, labels=False)) <= 1) .. link :: sage: p.solve() 5.0 sage: p.solve() 5.0 .. link :: sage: matching = p.get_values(matching) sage: [e for e,b in matching.iteritems() if b == 1] [(0, 1), (6, 9), (2, 7), (3, 4), (5, 8)] sage: matching = p.get_values(matching) sage: [e for e,b in matching.iteritems() if b == 1] [(0, 1), (6, 9), (2, 7), (3, 4), (5, 8)] Flows ----- Yet another fundamental algorithm in graph theory : maximum flow ! It consists, given a directed graph and two vertices s, t, in sending a maximum *flow* from s to t using the edges of G, each f them having a maximal capacity. Yet another fundamental algorithm in graph theory: maximum flow! It consists, given a directed graph and two vertices s, t, in sending a maximum *flow* from s to t using the edges of G, each of them having a maximal capacity. .. image:: static/lp_flot1.png :align: center .. image:: lp_flot1.png :align: center The definition of this problem is almost its LP formulation. We are looking for real values associted to each edge, which would represent the intensity of flow going through them, under two types of constraints: The definition of this problem is almost its LP formulation. We are looking for real values associted to each edge, which would representthe intensity of flow going through them, under two types of constraints: * The amount of flow arriving on a vertex (different from s or t) is equal to the amount of flow leaving it. * The amount of flow going through an edge is bounded by the capacity of this edge. * The amount of flow arriving on a vertex (different from s or t) is equal to the amount of flow leaving it * The amount of flow going through an edge is bounded by the capacity of this edge This being said, we but have to maximize the amount of flow leaving s : all of it will end up in t, as the other vertices are sending just as much as they receive. We can model the flow problem with the following LP This being said, we have to maximize the amount of flow leaving s: all of it will end up in t, as the other vertices are sending just as much as they receive. We can model the flow problem with the following LP .. MATH:: \text{Max : }&\sum_{sv\in G}f_{sv}\\ \text{Tel que : }&\forall v\in G, {v\neq s\atop v\neq t}, \sum_{vu\in G}f_{vu} - \sum_{uv \in G}f_{uv} = 0\\ &\forall uv\in G, f_{uv} \leq 1\\ \text{Max: } & \sum_{sv \in G} f_{sv}\\ \text{Such that: } & \forall v \in G, {v \neq s \atop v \neq t}, \sum_{vu \in G} f_{vu} - \sum_{uv \in G} f_{uv} = 0\\ & \forall uv \in G, f_{uv} \leq 1\\ We will he solve the flow problem on an orienration of Chvatal's Graph, in which all the edges have a capacity of 1:: We will solve the flow problem on an orientation of Chvatal's graph, in which all the edges have a capacity of 1:: sage: g = graphs.ChvatalGraph() sage: g = g.minimum_outdegree_orientation() sage: g = graphs.ChvatalGraph() sage: g = g.minimum_outdegree_orientation() .. link :: sage: p = MixedIntegerLinearProgram() sage: f = p.new_variable() sage: s, t = 0, 2 sage: p = MixedIntegerLinearProgram() sage: f = p.new_variable() sage: s, t = 0, 2 .. link :: sage: for v in g: ...      if v != s and v != t: ...          p.add_constraint( ...              sum( f[(v,u)] for u in g.neighbors_out(v)) ...             -sum( f[(u,v)] for u in g.neighbors_in(v)) == 0) sage: for v in g: ...       if v != s and v != t: ...           p.add_constraint( ...               sum(f[(v,u)] for u in g.neighbors_out(v)) ...               - sum(f[(u,v)] for u in g.neighbors_in(v)) == 0) .. link :: sage: for e in g.edges(labels = False): ...      p.add_constraint( f[e] <= 1 ) sage: for e in g.edges(labels=False): ...       p.add_constraint(f[e] <= 1) .. link :: sage: p.set_objective(sum( f[(s,u)] for u in g.neighbors_out(s))) sage: p.set_objective(sum(f[(s,u)] for u in g.neighbors_out(s))) .. link :: sage: p.solve() 2.0 sage: p.solve() 2.0 .. image:: lp_flot2.png :align: center .. image:: static/lp_flot2.png :align: center Solvers ------- Sage solves linear programs by calling specific libraries. The following libraries are currently supported : following libraries are currently supported: * CBC _: A solver from from COIN-OR _ (CPL -- Free) * CBC _: A solver from COIN-OR _ CBC can be installed through the command install_package("cbc") Provided under the open source license CPL, but incompatible with GPL. CBC can be installed through the command install_package("cbc"). * CPLEX _: A solver from ILOG _ (Proprietary, but free for researchers and students) A solver from ILOG _ Proprietary and closed source, but free for researchers and students. * GLPK _: A solver from GNU _ (GPL3, Free) _ This solver is installed by default with Sage Licensed under the GPLv3. This solver is installed by default with Sage. Installing CPLEX ---------------- ILOG CPLEX being Proprietary -- you must be in possession of several ILOG's CPLEX being proprietary, you must be in possession of several files to use it through Sage: * A valid license file * A compiled version of the CPLEX library (usually named libcplex.a) * The header file cplex.h * A valid license file. * A compiled version of the CPLEX library (usually named libcplex.a). * The header file cplex.h. The license file path must be set the value of the environment variable ILOG_LICENSE_FILE. For example, you can write :: variable ILOG_LICENSE_FILE. For example, you can write:: export ILOG_LICENSE_FILE=/path/to/the/license/ilog/ilm/access_1.ilm at the end of your .bashrc file. at the end of your .bashrc file. As Sage also needs the files libcplex.a and cplex.h, the easiest way is to create symbolic links toward these files in the appropriate directories : As Sage also needs the files libcplex.a and cplex.h, the easiest way is to create symbolic links to these files in the appropriate directories: * libcplex.a -- in SAGE_ROOT/local/lib/, type :: * libcplex.a -- in SAGE_ROOT/local/lib/, type:: ln -s /path/to/lib/libcplex.a . * cplex.h -- in SAGE_ROOT/local/include/, type :: * cplex.h -- in SAGE_ROOT/local/include/, type:: ln -s /path/to/include/cplex.h . Once this is done, and as CPLEX is used in Sage through the Osi library, which is part of the Cbc package, you can type:: library, which is part of the CBC package, you can type:: sage: install_package("cbc")  # not tested or, if you had already installed Cbc :: or, if you had already installed CBC:: sage: install_package("cbc", force = True)  # not tested sage: install_package("cbc", force=True)  # not tested to reinstall it.
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