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Table 1 Numerical results of Algorithm  A

From: A generalized gradient projection method based on a new working set for minimax optimization problems with inequality constraints

Prob.

n/l/m

\(x^{0}\)

NI

NF

NC

\(F(x^{*})\)

T (s)

P1

2/3/2

\((1,2.4)^{T}\)

15

115

33

1.952225

0.03

P2

2/3/2

\((0,1)^{T}\)

31

44

63

2.000009

0.01

P3

4/4/3

\((0,0.9,0.9, - 1.5)^{T}\)

28

143

89

−43.999992

0.01

P4

2/6/2

\((1,2.4)^{T}\)

18

379

37

0.616433

0.02

P5

50/2/48

\((2, \ldots,2)^{T}\)

85

551

4299

−69.296460

0.27

P6

50/2/48

\((0.5, \ldots,0.5)^{T}\)

150

6061

17026

−56.502976

0.79

P7

100/2/98

\((0.5, \ldots,0.5)^{T}\)

22

160

2157

0.000006

0.13

P8

100/100/98

\((1, \ldots,1)^{T}\)

98

691

9605

0.500009

1.22

P9

200/3/199

\((0.5, \ldots,0.5)^{T}\)

102

1087

22502

398.000010

2.72

P10

200/2/198

\((1, \ldots,1)^{T}\)

150

456

29723

111.701918

3.92