Kramer’s (a.k.a Cramer’s) Rule

Slides:



Advertisements
Similar presentations
Thursday, March 14 Introduction to Network Flows
Advertisements

BU BU Decision Models Networks 1 Networks Models Summer 2013.
Network Flows. 2 Ardavan Asef-Vaziri June-2013Transportation Problem and Related Topics Table of Contents Chapter 6 (Network Optimization Problems) Minimum-Cost.
1 Maximum flow sender receiver Capacity constraint Lecture 6: Jan 25.
Management Science 461 Lecture 6 – Network Flow Problems October 21, 2008.
Trees and BFSs Page 1 The Network Simplex Method Spanning Trees and Basic Feasible Solutions.
Applications of Maximum Flow and Minimum Cut Problems In this handout Transshipment problem Assignment Problem.
Network Optimization Models: Maximum Flow Problems In this handout: The problem statement Solving by linear programming Augmenting path algorithm.
Computer Algorithms Integer Programming ECE 665 Professor Maciej Ciesielski By DFG.
Linear Programming OPIM 310-Lecture 2 Instructor: Jose Cruz.
Linear Programming – Max Flow – Min Cut Orgad Keller.
NetworkModel-1 Network Optimization Models. NetworkModel-2 Network Terminology A network consists of a set of nodes and arcs. The arcs may have some flow.
Max-flow/min-cut theorem Theorem: For each network with one source and one sink, the maximum flow from the source to the destination is equal to the minimal.
1 Lecture 4 Maximal Flow Problems Set Covering Problems.
C&O 355 Mathematical Programming Fall 2010 Lecture 17 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A.
Lecture 4 – Network Flow Programming
Network Optimization Models
ENGM 732 Network Flow Programming Network Flow Models.
Section 2.9 Maximum Flow Problems Minimum Cost Network Flows Shortest Path Problems.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Applications of the Max-Flow Min-Cut Theorem. S-T Cuts SF D H C A NY S = {SF, D, H}, T={C,A,NY} [S,T] = {(D,A),(D,C),(H,A)}, Cap [S,T] =
EMIS 8373: Integer Programming “Easy” Integer Programming Problems: Network Flow Problems updated 11 February 2007.
C&O 355 Mathematical Programming Fall 2010 Lecture 18 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A.
1 1 Slide © 2009 South-Western, a part of Cengage Learning Slides by John Loucks St. Edward’s University.
Kramer’s (a.k.a Cramer’s) Rule Component j of x = A -1 b is Form B j by replacing column j of A with b.
Network Flow. Network flow formulation A network G = (V, E). Capacity c(u, v)  0 for edge (u, v). Assume c(u, v) = 0 if (u, v)  E. Source s and sink.
1 Network Models Transportation Problem (TP) Distributing any commodity from any group of supply centers, called sources, to any group of receiving.
EMIS 8374 Network Flow Models updated 29 January 2008.
Network Optimization Network optimization models: Special cases of linear programming models Important to identify problems that can be modeled as networks.
Lecture 5 – Integration of Network Flow Programming Models Topics Min-cost flow problem (general model) Mathematical formulation and problem characteristics.
Updated 21 April2008 Linear Programs with Totally Unimodular Matrices.
8/14/04J. Bard and J. W. Barnes Operations Research Models and Methods Copyright All rights reserved Lecture 5 – Integration of Network Flow Programming.
Chapter 8 Network Models to accompany Operations Research: Applications and Algorithms 4th edition by Wayne L. Winston Copyright (c) 2004 Brooks/Cole,
1 Chapter 2 Notation and Definitions Data Structures Transformations.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or.
C&O 355 Lecture 19 N. Harvey TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A.
Lecture 4 – Network Flow Programming Topics Terminology and Notation Network diagrams Generic problems (TP, AP, SPP, STP, MF) LP formulations Finding solutions.
ENGM 631 Maximum Flow Solutions. Maximum Flow Models (Flow, Capacity) (0,3) (2,2) (5,7) (0,8) (3,6) (6,8) (3,3) (4,4) (4,10)
1 Maximum Flows CONTENTS Introduction to Maximum Flows (Section 6.1) Introduction to Minimum Cuts (Section 6.1) Applications of Maximum Flows (Section.
Network Analyst. Network A network is a system of linear features that has the appropriate attributes for the flow of objects. A network is typically.
Polyhedral Optimization Lecture 2 – Part 2 M. Pawan Kumar Slides available online
Cycle Canceling Algorithm
Part 3 Linear Programming
Lap Chi Lau we will only use slides 4 to 19
St. Edward’s University
Topics in Algorithms Lap Chi Lau.
The minimum cost flow problem
The assignment problem
ENGM 535 Optimization Networks
Lecture 5 – Integration of Network Flow Programming Models
Lecture 5 – Integration of Network Flow Programming Models
1.3 Modeling with exponentially many constr.
Part 3 Linear Programming
1.206J/16.77J/ESD.215J Airline Schedule Planning
Transportation, Assignment and Network Models
Introduction Basic formulations Applications
Not Always Feasible 2 (3,5) 1 s (0,2) 3 (, u) t.
Kramer’s (a.k.a Cramer’s) Rule
TRLabs & University of Alberta © Wayne D. Grover 2002, 2003, 2004
and 6.855J Flow Decomposition
Chapter 5 Transportation, Assignment, and Transshipment Problems
Flow Feasibility Problems
Part 3 Linear Programming
The Network Simplex Method
Not Always Feasible 2 (3,5) 1 s (0,2) 3 (, u) t.
Network Optimization Models: Maximum Flow Problems
Maximum Flow Problems in 2005.
“Easy” Integer Programming Problems: Network Flow Problems
EMIS The Maximum Flow Problem: Flows and Cuts Updated 6 March 2008
Lecture 12 Network Models.
Presentation transcript:

Kramer’s (a.k.a Cramer’s) Rule Component j of x = A-1b is Form Bj by replacing column j of A with b.

Total Unimodularity A square, integer matrix B is unimodular (UM) if its determinant is 1 or -1. An integer matrix A is called totally unimodular (TUM) if every square, nonsingular submatrix of A is UM. From Cramer’s rule, it follows that if A is TUM and b is an integer vector, then every BFS of the constraint system Ax = b is integer.

TUM Theorem An integer matrix A is TUM if All entries are -1, 0 or 1 At most two non-zero entries appear in any column The rows of A can be partitioned into two disjoint sets such that If a column has two entries of the same sign, their rows are in different sets. If a column has two entries of different signs, their rows are in the same set. The MCNFP constraint matrices are TUM.

General Form of the MCNF Problem

Flow Balance Constraint Matrix 1 2 3 Capacity Constraints Constraints in Standard Form

Shortest Path Problems Defined on a Network Nodes, Arcs and Arc Costs Two Special Nodes Origin Node s Destination Node t A path from s to t is an alternating sequence of nodes and arcs starting at s and ending at t: s,(s,v1),v1,(v1,v2),…,(vi,vj),vj,(vj,t),t

s=1, t=3 We Want a Minimum Length Path From s to t. 1 5 2 10 3 7 1 7 4 1,(1,2),2,(2,3),3 Length = 15 1,(1,2),2,(2,4),4,(4,3) Length = 13 1,(1,4),4,(4,3),3 Length = 14

Maximizing Rent Example Optimally Select Non-Overlapping Bids for 10 periods

Shortest Path Formulation d10 -7 -2 d9 -3 -7 -5 -2 -1 -4 d1 d2 d3 d4 d5 d6 d7 d8 -3 s -6 -1 -11

MCNF Formulation of Shortest Path Problems Origin Node s has a supply of 1 Destination Node t has a demand of 1 All other Nodes are Transshipment Nodes Each Arc has Capacity 1 Tracing A Unit of Flow from s to t gives a Path from s to t

Maximum Flow Problems Defined on a Network Source Node s Sink Node t All Other Nodes are Transshipment Nodes Arcs have Capacities, but no Costs Maximize the Flow from s to t

Example: Rerouting Airline Passengers Due to a mechanical problem, Fly-By-Night Airlines had to cancel flight 162 - its only non-stop flight from San Francisco to New York. The table below shows the number of seats available on Fly-By-Night's other flights.

Formulate a maximum flow problem that will tell Fly-By-Night how to reroute as many passengers from San Francisco to New York as possible. SF D H C A NY 5 6 2 4 7 (flow, capacity) (2,2) (4,5) D C (2,4) Max Flow from SF to NY = 2+2+5=9 SF (2,4) NY (5,6) H A (7,7) (5,5)

MCNF Formulation of Maximum Flow Problems Let Arc Cost = 0 for all Arcs Add an infinite capacity arc from t to s Give this arc a cost of -1

Maximum-Flow Minimum-Cut Theorem SF D H C A NY 5 6 2 4 7 Removing arcs (D,C) and (A,NY) cuts off SF from NY. The set of arcs{(D,C), (A,NY)} is an s-t cut with capacity 2+7=9. The value of a maximum s-t flow = the capacity of a minimum s-t cut.