October  2019, 15(4): 1517-1534. doi: 10.3934/jimo.2018107

An interior point continuous path-following trajectory for linear programming

1. 

School of Science, Nanjing Audit University, Nanjing 211815, Jiangsu Province, China

2. 

Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China

* Corresponding author: Li-Zhi Liao

Received  January 2018 Revised  March 2018 Published  July 2018

Fund Project: The work of Liming Sun was supported in part by the National Natural Science Foundation of China (Grant No. 11701287) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20171071). The work of Li-Zhi Liao was supported in part by grants from the General Research Fund (GRF) of Hong Kong and FRG of Hong Kong Baptist University.

In this paper, an interior point continuous path-following trajectory is proposed for linear programming. The descent direction in our continuous trajectory can be viewed as some combination of the affine scaling direction and the centering direction for linear programming. A key component in our interior point continuous path-following trajectory is an ordinary differential equation (ODE) system. Various properties including the convergence in the limit for the solution of this ODE system are analyzed and discussed in detail. Several illustrative examples are also provided to demonstrate the numerical behavior of this continuous trajectory.

Citation: Liming Sun, Li-Zhi Liao. An interior point continuous path-following trajectory for linear programming. Journal of Industrial & Management Optimization, 2019, 15 (4) : 1517-1534. doi: 10.3934/jimo.2018107
References:
[1]

P.-A. AbsilR. Mahony and B. Andrews, Convergence of the iterates of descent methods for analytic cost functions, SIAM J. Optim., 16 (2005), 531-547.  doi: 10.1137/040605266.  Google Scholar

[2]

I. AdlerN. KarmarkarM. G. C. Resend and G. Veiga, An implementation of Karmarkar's algorithm for linear programming, Math. Program., 44 (1989), 297-335.  doi: 10.1007/BF01587095.  Google Scholar

[3]

N. Andrei, Gradient Flow Algorithm for Unconstrained Optimization, ICI Technical Report, April, 2004. Google Scholar

[4]

E. R. Barnes, A variation on Karmarkar's algorithm for solving linear programming problems, Math. Program., 36 (1986), 174-182.  doi: 10.1007/BF02592024.  Google Scholar

[5]

D. A. Bayer and J. C. Lagarias, The nonlinear geometry of linear programming. I Affine and projective scaling trajectories, Trans. Amer. Math. Soc., 314 (1989), 499-526.  doi: 10.2307/2001396.  Google Scholar

[6]

D. A. Bayer and J. C. Lagarias, The nonlinear geometry of linear programming. Ⅱ Legendre transform coordinates and central trajectories, Trans. Amer. Math. Soc., 314 (1989), 527-581.  doi: 10.2307/2001397.  Google Scholar

[7]

C. A. Botsaris, Differential gradient methods, J. Math. Anal. Appl., 63 (1978), 177-198.  doi: 10.1016/0022-247X(78)90114-2.  Google Scholar

[8]

F. H. Branin, A widely convergent method for finding multiple solutions of simultaneous nonlinear equations, IBM J. Res. Devel., 16 (1972), 504-522.  doi: 10.1147/rd.165.0504.  Google Scholar

[9]

F. H. Branin and S. K. Hoo, A method for finding multiple extrema of a function of N variables, Numerical Methods for Non-Linear Optimization (Conf., Univ. Dundee, Dundee, 1971), Academic Press, London, (1972), 231-237.  Google Scholar

[10]

A. A. Brown and M. C. Bartholomew-Biggs, Some effective methods for unconstrained optimization based on the solution of systems of ordinary differential equations, J. Optim. Theory Appl., 62 (1989), 211-224.  doi: 10.1007/BF00941054.  Google Scholar

[11]

R. Courant, Variational methods for the solution of problems of equilibrium and vibration, Bull. Amer. Math. Soc., 49 (1943), 1-23.  doi: 10.1090/S0002-9904-1943-07818-4.  Google Scholar

[12]

J. E. Dennis, Jr. and R. B. Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, SIAM, 1996. doi: 10.1137/1.9781611971200.  Google Scholar

[13]

I. Diener, On the global convergence of path-following methods to determine all solutions to a system of nonlinear equations, Math. Program., 39 (1987), 181-188.  doi: 10.1007/BF02592951.  Google Scholar

[14]

I. Diener, Trajectory nets connecting all critical points of a smooth function, Math. Program., 36 (1986), 340-352.  doi: 10.1007/BF02592065.  Google Scholar

[15]

I. I. Dikin, Iterative solution of problems of linear and qudartic programming, Doklady Akademiia Nauk SSSR, 174 (1967), 747-748.   Google Scholar

[16]

R. M. Freund, Polynomial-time algorithms for linear programming based only on primal scaling and projected gradients of a potential function, Math. Program., 51 (1991), 203-222.  doi: 10.1007/BF01586933.  Google Scholar

[17]

P. E. GillW. MurrayM. A. SaundersJ. A. Tomlin and M. H. Wright, On projected Newton barrier methods for linear programming and an equivalence to Karmarkar's projective method, Math. Program., 36 (1986), 183-209.  doi: 10.1007/BF02592025.  Google Scholar

[18]

C. C. Gonzaga, Polynomial affine algorithms for linear programming, Math. Program., 49 (1990/91), 7-21.  doi: 10.1007/BF01588776.  Google Scholar

[19]

C. C. Gonzaga, Large step path-following methods for linear programming. Ⅱ. Potential reduction method, SIAM J. Optim., 1 (1991), 280-292.  doi: 10.1137/0801019.  Google Scholar

[20]

C. C. Gonzaga, Path-following methods for linear programming, SIAM Rev., 34 (1992), 167-224.  doi: 10.1137/1034048.  Google Scholar

[21]

N. Karmarkar, A new polynomial-time algorithm for linear programming, Combinatorica, 4 (1984), 373-395.  doi: 10.1007/BF02579150.  Google Scholar

[22]

L.-Z. LiaoH. D. Qi and L. Q. Qi, Neurodynamical optimization, J. Global Optim, 28 (2004), 175-195.  doi: 10.1023/B:JOGO.0000015310.27011.02.  Google Scholar

[23]

L.-Z. Liao, A study of the dual affine scaling continuous trajectories for linear programming, J. Optim. Theory and Appl., 163 (2014), 548-568.  doi: 10.1007/s10957-013-0495-1.  Google Scholar

[24]

N. Megiddo and M. Shub, Boundary behavior of interior point algorihms for linear programming, Math. Oper. Res., 14 (1989), 97-146.  doi: 10.1287/moor.14.1.97.  Google Scholar

[25]

C. L. Monma and J. Morton, Computational experience with a dual affine variant of Karmarkar's method for linear programming, Oper. Res. Lett., 6 (1987), 261-267.  doi: 10.1016/0167-6377(87)90040-X.  Google Scholar

[26]

R. D. C. Monteiro and I. Adler, Interior path following primal-dual algorithms. I. Linear programming, Math. Program., 44 (1989), 27-41.  doi: 10.1007/BF01587075.  Google Scholar

[27]

R. D. C. MonteiroI. Adler and M. G. C. Resende, A polynomial-time primal-dual affine scaling algorithm for linear and convex quadratic programming and its power series extension, Math. Oper. Res., 15 (1990), 191-214.  doi: 10.1287/moor.15.2.191.  Google Scholar

[28]

R. D. C. Monteiro and I. Adler, Limiting behavior of the affine scaling continuous trajectories for linear programming problems, Math. Program., 50 (1991), 29-51.  doi: 10.1007/BF01594923.  Google Scholar

[29]

R. D. C. Monteiro, On the continuous trajectories for a potential reduction algorithm for linear programming, Math. Oper. Res., 17 (1992), 225-253.  doi: 10.1287/moor.17.1.225.  Google Scholar

[30]

X. Qian and L.-Z. Liao, Analysis of the primal affine scaling continuous trajectory for convex programming, Pacific J. Optimi., (to appear). Google Scholar

[31]

C. Roos, New trajectory-following polynomial-time algorithm for linear programming problems, J. Optim. Theory Appl., 63 (1989), 433-458.  doi: 10.1007/BF00939806.  Google Scholar

[32]

C. Roos and J.-Ph. Vial, A polynomial method of approximate centers for linear programming, Math. Program., 54 (1992), 295-305.  doi: 10.1007/BF01586056.  Google Scholar

[33]

J. J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall, New Jersey, 1991. Google Scholar

[34]

G. W. Stewart, On scaled projections and pseudoinverses, Linear Alg. Appl., 112 (1989), 189-193.  doi: 10.1016/0024-3795(89)90594-6.  Google Scholar

[35]

J. Sun, A convergence proof for an affine scaling algorithm for convex quadratic programming without nondegeneracy assumptions, Math. Program., 60 (1993), 69-79.  doi: 10.1007/BF01580601.  Google Scholar

[36]

J. Sun, A convergence analysis for a convex version of Dikin's algorithm, Annals Oper. Res., 62 (1996), 357-374.  doi: 10.1007/BF02206823.  Google Scholar

[37]

M. J. Todd, A Dantzig-Wolfe-like variant of Karmarkar's interior-point linear programming algorithm, Oper. Res., 38 (1990), 1006-1018.  doi: 10.1287/opre.38.6.1006.  Google Scholar

[38]

P. Tseng and Z.-Q. Luo, On the convergence of the affine-scaling algorithm, Math. Program., 56 (1992), 301-319.  doi: 10.1007/BF01580904.  Google Scholar

[39]

T. Tsuchiya, Affine scaling algorithm, Interior Point Methods of Mathematical Programming, Kluwer Academic Pub., Netherlands, 5 (1996), 35-82. doi: 10.1007/978-1-4613-3449-1_2.  Google Scholar

[40]

R. J. VanderbeiM. S. Meketon and B. A. Freedman, A modification of Karmarkar's linear programming algorithm, Algorithmica, 1 (1986), 395-407.  doi: 10.1007/BF01840454.  Google Scholar

[41]

C. Witzgall, P. T. Boggs and P. D. Domich, On the convergence behavior of trajectories for linear programming, Mathematical Developments Arising from Linear Programming (Brunswick, ME, 1988), 161-187, Contemp. Math., 114, Amer. Math. Soc., Providence, RI, 1990. doi: 10.1090/conm/114/1097873.  Google Scholar

show all references

References:
[1]

P.-A. AbsilR. Mahony and B. Andrews, Convergence of the iterates of descent methods for analytic cost functions, SIAM J. Optim., 16 (2005), 531-547.  doi: 10.1137/040605266.  Google Scholar

[2]

I. AdlerN. KarmarkarM. G. C. Resend and G. Veiga, An implementation of Karmarkar's algorithm for linear programming, Math. Program., 44 (1989), 297-335.  doi: 10.1007/BF01587095.  Google Scholar

[3]

N. Andrei, Gradient Flow Algorithm for Unconstrained Optimization, ICI Technical Report, April, 2004. Google Scholar

[4]

E. R. Barnes, A variation on Karmarkar's algorithm for solving linear programming problems, Math. Program., 36 (1986), 174-182.  doi: 10.1007/BF02592024.  Google Scholar

[5]

D. A. Bayer and J. C. Lagarias, The nonlinear geometry of linear programming. I Affine and projective scaling trajectories, Trans. Amer. Math. Soc., 314 (1989), 499-526.  doi: 10.2307/2001396.  Google Scholar

[6]

D. A. Bayer and J. C. Lagarias, The nonlinear geometry of linear programming. Ⅱ Legendre transform coordinates and central trajectories, Trans. Amer. Math. Soc., 314 (1989), 527-581.  doi: 10.2307/2001397.  Google Scholar

[7]

C. A. Botsaris, Differential gradient methods, J. Math. Anal. Appl., 63 (1978), 177-198.  doi: 10.1016/0022-247X(78)90114-2.  Google Scholar

[8]

F. H. Branin, A widely convergent method for finding multiple solutions of simultaneous nonlinear equations, IBM J. Res. Devel., 16 (1972), 504-522.  doi: 10.1147/rd.165.0504.  Google Scholar

[9]

F. H. Branin and S. K. Hoo, A method for finding multiple extrema of a function of N variables, Numerical Methods for Non-Linear Optimization (Conf., Univ. Dundee, Dundee, 1971), Academic Press, London, (1972), 231-237.  Google Scholar

[10]

A. A. Brown and M. C. Bartholomew-Biggs, Some effective methods for unconstrained optimization based on the solution of systems of ordinary differential equations, J. Optim. Theory Appl., 62 (1989), 211-224.  doi: 10.1007/BF00941054.  Google Scholar

[11]

R. Courant, Variational methods for the solution of problems of equilibrium and vibration, Bull. Amer. Math. Soc., 49 (1943), 1-23.  doi: 10.1090/S0002-9904-1943-07818-4.  Google Scholar

[12]

J. E. Dennis, Jr. and R. B. Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, SIAM, 1996. doi: 10.1137/1.9781611971200.  Google Scholar

[13]

I. Diener, On the global convergence of path-following methods to determine all solutions to a system of nonlinear equations, Math. Program., 39 (1987), 181-188.  doi: 10.1007/BF02592951.  Google Scholar

[14]

I. Diener, Trajectory nets connecting all critical points of a smooth function, Math. Program., 36 (1986), 340-352.  doi: 10.1007/BF02592065.  Google Scholar

[15]

I. I. Dikin, Iterative solution of problems of linear and qudartic programming, Doklady Akademiia Nauk SSSR, 174 (1967), 747-748.   Google Scholar

[16]

R. M. Freund, Polynomial-time algorithms for linear programming based only on primal scaling and projected gradients of a potential function, Math. Program., 51 (1991), 203-222.  doi: 10.1007/BF01586933.  Google Scholar

[17]

P. E. GillW. MurrayM. A. SaundersJ. A. Tomlin and M. H. Wright, On projected Newton barrier methods for linear programming and an equivalence to Karmarkar's projective method, Math. Program., 36 (1986), 183-209.  doi: 10.1007/BF02592025.  Google Scholar

[18]

C. C. Gonzaga, Polynomial affine algorithms for linear programming, Math. Program., 49 (1990/91), 7-21.  doi: 10.1007/BF01588776.  Google Scholar

[19]

C. C. Gonzaga, Large step path-following methods for linear programming. Ⅱ. Potential reduction method, SIAM J. Optim., 1 (1991), 280-292.  doi: 10.1137/0801019.  Google Scholar

[20]

C. C. Gonzaga, Path-following methods for linear programming, SIAM Rev., 34 (1992), 167-224.  doi: 10.1137/1034048.  Google Scholar

[21]

N. Karmarkar, A new polynomial-time algorithm for linear programming, Combinatorica, 4 (1984), 373-395.  doi: 10.1007/BF02579150.  Google Scholar

[22]

L.-Z. LiaoH. D. Qi and L. Q. Qi, Neurodynamical optimization, J. Global Optim, 28 (2004), 175-195.  doi: 10.1023/B:JOGO.0000015310.27011.02.  Google Scholar

[23]

L.-Z. Liao, A study of the dual affine scaling continuous trajectories for linear programming, J. Optim. Theory and Appl., 163 (2014), 548-568.  doi: 10.1007/s10957-013-0495-1.  Google Scholar

[24]

N. Megiddo and M. Shub, Boundary behavior of interior point algorihms for linear programming, Math. Oper. Res., 14 (1989), 97-146.  doi: 10.1287/moor.14.1.97.  Google Scholar

[25]

C. L. Monma and J. Morton, Computational experience with a dual affine variant of Karmarkar's method for linear programming, Oper. Res. Lett., 6 (1987), 261-267.  doi: 10.1016/0167-6377(87)90040-X.  Google Scholar

[26]

R. D. C. Monteiro and I. Adler, Interior path following primal-dual algorithms. I. Linear programming, Math. Program., 44 (1989), 27-41.  doi: 10.1007/BF01587075.  Google Scholar

[27]

R. D. C. MonteiroI. Adler and M. G. C. Resende, A polynomial-time primal-dual affine scaling algorithm for linear and convex quadratic programming and its power series extension, Math. Oper. Res., 15 (1990), 191-214.  doi: 10.1287/moor.15.2.191.  Google Scholar

[28]

R. D. C. Monteiro and I. Adler, Limiting behavior of the affine scaling continuous trajectories for linear programming problems, Math. Program., 50 (1991), 29-51.  doi: 10.1007/BF01594923.  Google Scholar

[29]

R. D. C. Monteiro, On the continuous trajectories for a potential reduction algorithm for linear programming, Math. Oper. Res., 17 (1992), 225-253.  doi: 10.1287/moor.17.1.225.  Google Scholar

[30]

X. Qian and L.-Z. Liao, Analysis of the primal affine scaling continuous trajectory for convex programming, Pacific J. Optimi., (to appear). Google Scholar

[31]

C. Roos, New trajectory-following polynomial-time algorithm for linear programming problems, J. Optim. Theory Appl., 63 (1989), 433-458.  doi: 10.1007/BF00939806.  Google Scholar

[32]

C. Roos and J.-Ph. Vial, A polynomial method of approximate centers for linear programming, Math. Program., 54 (1992), 295-305.  doi: 10.1007/BF01586056.  Google Scholar

[33]

J. J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall, New Jersey, 1991. Google Scholar

[34]

G. W. Stewart, On scaled projections and pseudoinverses, Linear Alg. Appl., 112 (1989), 189-193.  doi: 10.1016/0024-3795(89)90594-6.  Google Scholar

[35]

J. Sun, A convergence proof for an affine scaling algorithm for convex quadratic programming without nondegeneracy assumptions, Math. Program., 60 (1993), 69-79.  doi: 10.1007/BF01580601.  Google Scholar

[36]

J. Sun, A convergence analysis for a convex version of Dikin's algorithm, Annals Oper. Res., 62 (1996), 357-374.  doi: 10.1007/BF02206823.  Google Scholar

[37]

M. J. Todd, A Dantzig-Wolfe-like variant of Karmarkar's interior-point linear programming algorithm, Oper. Res., 38 (1990), 1006-1018.  doi: 10.1287/opre.38.6.1006.  Google Scholar

[38]

P. Tseng and Z.-Q. Luo, On the convergence of the affine-scaling algorithm, Math. Program., 56 (1992), 301-319.  doi: 10.1007/BF01580904.  Google Scholar

[39]

T. Tsuchiya, Affine scaling algorithm, Interior Point Methods of Mathematical Programming, Kluwer Academic Pub., Netherlands, 5 (1996), 35-82. doi: 10.1007/978-1-4613-3449-1_2.  Google Scholar

[40]

R. J. VanderbeiM. S. Meketon and B. A. Freedman, A modification of Karmarkar's linear programming algorithm, Algorithmica, 1 (1986), 395-407.  doi: 10.1007/BF01840454.  Google Scholar

[41]

C. Witzgall, P. T. Boggs and P. D. Domich, On the convergence behavior of trajectories for linear programming, Mathematical Developments Arising from Linear Programming (Brunswick, ME, 1988), 161-187, Contemp. Math., 114, Amer. Math. Soc., Providence, RI, 1990. doi: 10.1090/conm/114/1097873.  Google Scholar

Figure 1.  Transient behaviors of the continuous path of $x(t)$ and the objective function $c^Tx$ in Example 4.1 with starting point $x_0$
Figure 2.  Transient behaviors of the continuous path of $x(t)$ and the objective function $c^Tx$ in Example 4.1 with starting point $x_0^{'}$
Figure 3.  Transient behaviors of the continuous path of $x(t)$ and the objective function $c^Tx$ in Example 4.2 with starting point $x_0$
Figure 4.  Transient behaviors of the continuous path of $x(t)$ and the objective function $c^Tx$ in Example 4.2 with starting point $x_0^{'}$
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