这里写自定义目录标题
- Direct FP
- Closed-Form FP
- the Lagrangian function
- the Lagrange dual function: maximizing the Lagrangian
- the Lagrange dual problem: minimizing the Lagrange dual function
- Closed-Form FP
- Weighted MMSE
- 原论文
- Lagrange dual
- 5.1.1 The Lagrangian
- 5.1.2 The Lagrange dual function
- 5.2 The Lagrange dual problem
- 5.2.3 Strong duality and Slater’s constraint qualification
- 5.2.3 Strong duality and Slater’s constraint qualification
- 5.5.3 KKT optimality conditions
- 仿真
Multi-User in each Cell, MISO
沈闓明代码
Direct FP
K. Shen and W. Yu, “Fractional Programming for Communication Systems—Part I: Power Control and Beamforming,” in IEEE Transactions on Signal Processing, vol. 66, no. 10, pp. 2616-2630, 15 May15, 2018, doi: 10.1109/TSP.2018.2812733.
∑ n = 1 N ∑ k = 1 K log 2 ( 1 + ∣ h n , n , k H w n , k ∣ 2 ∑ ( j , i ) ≠ ( n , k ) ∣ h j , n , k H w j , i ∣ 2 + σ n , k 2 ) \sum\limits_{n = 1}^N {\sum\limits_{k = 1}^K {{{\log }_2}\left( {1 + \frac{{{{\left| {{\bf{h}}_{n,n,k}^H{{\bf{w}}_{n,k}}} \right|}^2}}}{{\sum\limits_{(j,i) \ne (n,k)} {{{\left| {{\bf{h}}_{j,n,k}^H{{\bf{w}}_{j,i}}} \right|}^2}} + \sigma _{n,k}^2}}} \right)} } n=1∑Nk=1∑Klog2 1+(j,i)=(n,k)∑∣hj,n,kHwj,i∣2+σn,k2∣hn,n,kHwn,k∣2
the direct FP approach applies the multidimensional quadratic transform (Theorem 2) to each SINR term.
f q ( W , Y ) = ∑ ( n , k ) log ( 1 + 2 R e { y n , k H w n , k H h n , n , k } − ∣ y n , k ∣ 2 ( ∑ ( j , i ) ≠ ( n , k ) ∣ h j , n , k H w j , i ∣ 2 + σ n , k 2 ) ) {f_q}\left( {{\bf{W}},{\bf{Y}}} \right) = \sum\limits_{(n,k)} {\log \left( {1 + 2{\rm{Re}}\left\{ {y_{n,k}^H{\bf{w}}_{n,k}^H{{\bf{h}}_{n,n,k}}} \right\} - {{\left| {{y_{n,k}}} \right|}^2}\left( {\sum\limits_{(j,i) \ne (n,k)} {{{\left| {{\bf{h}}_{j,n,k}^H{{\bf{w}}_{j,i}}} \right|}^2}} + \sigma _{n,k}^2} \right)} \right)} fq(W,Y)=(n,k)∑log(1+2Re{yn,kHwn,kHhn,n,k}−∣yn,k∣2((j,i)=(n,k)∑ hj,n,kHwj,i 2+σn,k2))
初始化 w n , k , ∀ n , k {\bf{w}}_{n,k}, \forall n,k wn,k,∀n,k
重复
- 更新 y n , k ⋆ = h n , n , k H w n , k ∑ ( j , i ) ≠ ( n , k ) ∣ h j , n , k H w j , i ∣ 2 + σ n , k 2 y_{n,k}^ \star = \frac{{{\bf{h}}_{n,n,k}^H{{\bf{w}}_{n,k}}}}{{\sum\limits_{(j,i) \ne (n,k)} {{{\left| {{\bf{h}}_{j,n,k}^H{{\bf{w}}_{j,i}}} \right|}^2}} + \sigma _{n,k}^2}} yn,k⋆=(j,i)=(n,k)∑∣hj,n,kHwj,i∣2+σn,k2hn,n,kHwn,k with w n , k , ∀ n , k {\bf{w}}_{n,k}, \forall n,k wn,k,∀n,k
- 给定 y n , k {y_{n,k}} yn,k,求解问题,更新 w n , k {{\bf{w}}_{n,k}} wn,k
max { w n , k , y n , k } f q ( W , Y ) s . t . ∑ k = 1 K w n , k H w n , k ≤ p ˉ n , ∀ n = 1 , … , N , \begin{array}{l} \mathop {\max }\limits_{\left\{ {{{\bf{w}}_{n,k}},{y_{n,k}}} \right\}} \;\;{f_q}\left( {{\bf{W}},{\bf{Y}}} \right)\\ {\rm{s}}.{\rm{t}}.\;\sum\limits_{k = 1}^K {{\bf{w}}_{n,k}^H{{\bf{w}}_{n,k}}} \le {{\bar p}_n},\forall n = 1, \ldots ,N, \end{array} {wn,k,yn,k}maxfq(W,Y)s.t.k=1∑Kwn,kHwn,k≤pˉn,∀n=1,…,N,
the optimization problem is a convex problem of w n , k {{\bf{w}}_{n,k}} wn,k when the auxiliary variable y n , k {y_{n,k}} yn,k is held fixed.
直到 f q ( W , Y ) {f_q}\left( {{\bf{W}},{\bf{Y}}} \right) fq(W,Y)收敛
Closed-Form FP
K. Shen and W. Yu, “Fractional Programming for Communication Systems—Part I: Power Control and Beamforming,” in IEEE Transactions on Signal Processing, vol. 66, no. 10, pp. 2616-2630, 15 May15, 2018, doi: 10.1109/TSP.2018.2812733.
∑ n = 1 N ∑ k = 1 K log 2 ( 1 + ∣ h n , n , k H w n , k ∣ 2 ∑ ( j , i ) ≠ ( n , k ) ∣ h j , n , k H w j , i ∣ 2 + σ n , k 2 ) \sum\limits_{n = 1}^N {\sum\limits_{k = 1}^K {{{\log }_2}\left( {1 + \frac{{{{\left| {{\bf{h}}_{n,n,k}^H{{\bf{w}}_{n,k}}} \right|}^2}}}{{\sum\limits_{(j,i) \ne (n,k)} {{{\left| {{\bf{h}}_{j,n,k}^H{{\bf{w}}_{j,i}}} \right|}^2}} + \sigma _{n,k}^2}}} \right)} } n=1∑Nk=1∑Klog2 1+(j,i)=(n,k)∑∣hj,n,kHwj,i∣2+σn,k2∣hn,n,kHwn,k∣2
Lagrangian Dual Transform (Multidimensional and Complex)
f r ( W , U ) = ∑ ( n , k ) ( log ( 1 + u n , k ) − u n , k + ( 1 + u n , k ) ∣ h n , n , k H w n , k ∣ 2 ∑ ( j , i ) ∣ h j , n , k H w j , i ∣ 2 + σ n , k 2 ) {f_r}\left( {{\bf{W}},{\bf{U}}} \right) = \sum\limits_{(n,k)} {\left( {\log \left( {1 + {u_{n,k}}} \right) - {u_{n,k}} + \left( {1 + {u_{n,k}}} \right)\frac{{{{\left| {{\bf{h}}_{n,n,k}^H{{\bf{w}}_{n,k}}} \right|}^2}}}{{\sum\limits_{(j,i)} {{{\left| {{\bf{h}}_{j,n,k}^H{{\bf{w}}_{j,i}}} \right|}^2}} + \sigma _{n,k}^2}}} \right)} fr(W,U)=(n,k)∑ log(1+un,k)−un,k+(1+un,k)(j,i)∑∣hj,n,kHwj,i∣2+σn,k2∣hn,n,kHwn,k∣2
∂ ∂ u n , k f r ( W , U ) = 0 \frac{\partial }{{\partial {u_{n,k}}}}{f_r}\left( {{\bf{W}},{\bf{U}}} \right) = 0 ∂un,k∂fr(W,U)=0
u n , k ⋆ = γ n , k = ∣ h n , n , k H w n , k ∣ 2 ∑ ( j , i ) ≠ ( n , k ) ∣ h j , n , k H w j , i ∣ 2 + σ n , k 2 ∈ R 1 u_{n,k}^ \star = {\gamma _{n,k}} = \frac{{{{\left| {{\bf{h}}_{n,n,k}^H{{\bf{w}}_{n,k}}} \right|}^2}}}{{\sum\limits_{(j,i) \ne (n,k)} {{{\left| {{\bf{h}}_{j,n,k}^H{{\bf{w}}_{j,i}}} \right|}^2}} + \sigma _{n,k}^2}} \in {{\mathbb{R}}^1} un,k⋆=γn,k=(j,i)=(n,k)∑∣hj,n,kHwj,i∣2+σn,k2∣hn,n,kHwn,k∣2∈R1
Quadratic Transform (Multidimensional)
f q ( W , U , V ) = ∑ ( n , k ) ( 2 ( 1 + u n , k ) R e { w n , k H h n , n , k v n , k } − ∣ v n , k ∣ 2 ( ∑ ( j , i ) ∣ h j , n , k H w j , i ∣ 2 + σ n , k 2 ) ) + c o n s t ( U ) {f_q}\left( {{\bf{W}},{\bf{U}},{\bf{V}}} \right) = \sum\limits_{(n,k)} {\left( {2\sqrt {(1 + {u_{n,k}})} {\rm{Re}}\left\{ {{\bf{w}}_{n,k}^H{{\bf{h}}_{n,n,k}}{v_{n,k}}} \right\} - {{\left| {{v_{n,k}}} \right|}^2}\left( {\sum\limits_{(j,i)} {{{\left| {{\bf{h}}_{j,n,k}^H{{\bf{w}}_{j,i}}} \right|}^2}} + \sigma _{n,k}^2} \right)} \right)} + {\rm{const}}({\bf{U}}) fq(W,U,V)=(n,k)∑(2(1+un,k)Re{wn,kHhn,n,kvn,k}−∣vn,k∣2((j,i)∑ hj,n,kHwj,i 2+σn,k2))+const(U)
∂ ∂ v n , k f q ( W , U , V ) = 0 \frac{\partial }{{\partial {v_{n,k}}}}{f_q}\left( {{\bf{W}},{\bf{U}},{\bf{V}}} \right) = 0 ∂vn,k∂fq(W,U,V)=0
v n , k ⋆ = ( 1 + u n , k ) h n , n , k H w n , k ∑ ( j , i ) ∣ h j , n , k H w j , i ∣ 2 + σ n , k 2 v_{n,k}^ \star = \frac{{\sqrt {(1 + {u_{n,k}})} {\bf{h}}_{n,n,k}^H{{\bf{w}}_{n,k}}}}{{\sum\limits_{(j,i)} {{{\left| {{\bf{h}}_{j,n,k}^H{{\bf{w}}_{j,i}}} \right|}^2}} + \sigma _{n,k}^2}} vn,k⋆=(j,i)∑∣hj,n,kHwj,i∣2+σn,k2(1+un,k)hn,n,kHwn,k
Transformed Problem
max { w n , k } f q ( W , U , V ) s . t . p ˉ n − ∑ k = 1 K w n , k H w n , k ≥ 0 , ∀ n = 1 , … , N , \begin{array}{l} \mathop {\max }\limits_{\left\{ {{{\bf{w}}_{n,k}}} \right\}} \;\;{f_q}\left( {{\bf{W}},{\bf{U}},{\bf{V}}} \right)\\ {\rm{s}}.{\rm{t}}.\;{{\bar p}_n} - \sum\limits_{k = 1}^K {{\bf{w}}_{n,k}^H{{\bf{w}}_{n,k}}} \ge 0,\forall n = 1, \ldots ,N, \end{array} {wn,k}maxfq(W,U,V)s.t.pˉn−k=1∑Kwn,kHwn,k≥0,∀n=1,…,N,
the Lagrangian function
L ( W , U , V , η ) = f q ( W , U , V ) + ∑ n = 1 N η n ( p ˉ n − ∑ k = 1 K w n , k H w n , k ) L({\bf{W}},{\bf{U}},{\bf{V}},{\bm{\eta}}) = {f_q}({\bf{W}},{\bf{U}},{\bf{V}}) + \sum\limits_{n = 1}^N {{\eta _n}\left( {{{\bar p}_n} - \sum\limits_{k = 1}^K {{\bf{w}}_{n,k}^H{{\bf{w}}_{n,k}}} } \right)} L(W,U,V,η)=fq(W,U,V)+n=1∑Nηn(pˉn−k=1∑Kwn,kHwn,k)
the Lagrange dual function: maximizing the Lagrangian
g ( η ) = m a x { w n , k } L ( W , U , V , η ) g\left( {\bm{\eta}} \right) = \mathop {{\rm{max}}}\limits_{\left\{ {{{\bf{w}}_{n,k}}} \right\}} \;L({\bf{W}},{\bf{U}},{\bf{V}},{\bm{\eta}}) g(η)={wn,k}maxL(W,U,V,η)
∂ ∂ w n , k L ( W , U , V , η ) = 0 ⇒ \frac{\partial }{{\partial {{\bf{w}}_{n,k}}}}L({\bf{W}},{\bf{U}},{\bf{V}},{\bm{\eta}}) = 0 \Rightarrow ∂wn,k∂L(W,U,V,η)=0⇒
w n , k ∗ = ( 1 + u n , k ) h n , n , k v n , k ∑ ( m , l ) ( h n , m , l v m , l v m , l H h n , m , l H ) + η n I {\bf{w}}_{n,k}^* = \frac{{\sqrt {(1 + {u_{n,k}})} {{\bf{h}}_{n,n,k}}{v_{n,k}}}}{{\sum\limits_{(m,l)} {\left( {{{\bf{h}}_{n,m,l}}{v_{m,l}}v_{m,l}^H{\bf{h}}_{n,m,l}^H} \right)} + {\eta _n}I}} wn,k∗=(m,l)∑(hn,m,lvm,lvm,lHhn,m,lH)+ηnI(1+un,k)hn,n,kvn,k
the Lagrange dual problem: minimizing the Lagrange dual function
Lagrange multipliers are component-wise non-negative
m i n η ≥ 0 g ( η ) \mathop {{\rm{min}}}\limits_{{\bm{\eta}} \ge 0} g\left( {\bm{\eta}} \right) η≥0ming(η)
Closed-Form FP
初始化 w n , k , ∀ n , k {\bf{w}}_{n,k}, \forall n,k wn,k,∀n,k
重复
- 更新 u n , k ⋆ = γ n , k = ∣ h n , n , k H w n , k ∣ 2 ∑ ( j , i ) ≠ ( n , k ) ∣ h j , n , k H w j , i ∣ 2 + σ n , k 2 ∈ R 1 u_{n,k}^ \star = {\gamma _{n,k}} = \frac{{{{\left| {{\bf{h}}_{n,n,k}^H{{\bf{w}}_{n,k}}} \right|}^2}}}{{\sum\limits_{(j,i) \ne (n,k)} {{{\left| {{\bf{h}}_{j,n,k}^H{{\bf{w}}_{j,i}}} \right|}^2}} + \sigma _{n,k}^2}} \in {{\mathbb{R}}^1} un,k⋆=γn,k=(j,i)=(n,k)∑∣hj,n,kHwj,i∣2+σn,k2∣hn,n,kHwn,k∣2∈R1 with w n , k , ∀ n , k {\bf{w}}_{n,k}, \forall n,k wn,k,∀n,k
- 更新 v n , k ⋆ = ( 1 + u n , k ) h n , n , k H w n , k ∑ ( j , i ) ∣ h j , n , k H w j , i ∣ 2 + σ n , k 2 v_{n,k}^ \star = \frac{{\sqrt {(1 + {u_{n,k}})} {\bf{h}}_{n,n,k}^H{{\bf{w}}_{n,k}}}}{{\sum\limits_{(j,i)} {{{\left| {{\bf{h}}_{j,n,k}^H{{\bf{w}}_{j,i}}} \right|}^2}} + \sigma _{n,k}^2}} vn,k⋆=(j,i)∑∣hj,n,kHwj,i∣2+σn,k2(1+un,k)hn,n,kHwn,k with w n , k , ∀ n , k {\bf{w}}_{n,k}, \forall n,k wn,k,∀n,k and u n , k , ∀ n , k u_{n,k}, \forall n,k un,k,∀n,k
- 更新 η n , ∀ n {\eta _n}, \forall n ηn,∀n。利用二分法可以找到最小的 η n {\eta _n} ηn ,使得 ∑ k = 1 K w n , k H ( η n ) w n , k ( η n ) = p ˉ n \sum\limits_{k = 1}^K {{\bf{w}}_{n,k}^H\left( {{\eta _n}} \right){{\bf{w}}_{n,k}}\left( {{\eta _n}} \right)} = {\bar p_n} k=1∑Kwn,kH(ηn)wn,k(ηn)=pˉn 。
其中, w n , k ( η n ) = ( 1 + u n , k ) h n , n , k v n , k ∑ ( m , l ) ( h n , m , l v m , l v m , l H h n , m , l H ) + η n I {{\bf{w}}_{n,k}}\left( {{\eta _n}} \right) = \frac{{\sqrt {(1 + {u_{n,k}})} {{\bf{h}}_{n,n,k}}{v_{n,k}}}}{{\sum\limits_{(m,l)} {\left( {{{\bf{h}}_{n,m,l}}{v_{m,l}}v_{m,l}^H{\bf{h}}_{n,m,l}^H} \right)} + {\eta _n}I}} wn,k(ηn)=(m,l)∑(hn,m,lvm,lvm,lHhn,m,lH)+ηnI(1+un,k)hn,n,kvn,k - 更新 w n , k ∗ = ( 1 + u n , k ) h n , n , k v n , k ∑ ( m , l ) ( h n , m , l v m , l v m , l H h n , m , l H ) + η n I {\bf{w}}_{n,k}^* = \frac{{\sqrt {(1 + {u_{n,k}})} {{\bf{h}}_{n,n,k}}{v_{n,k}}}}{{\sum\limits_{(m,l)} {\left( {{{\bf{h}}_{n,m,l}}{v_{m,l}}v_{m,l}^H{\bf{h}}_{n,m,l}^H} \right)} + {\eta _n}I}} wn,k∗=(m,l)∑(hn,m,lvm,lvm,lHhn,m,lH)+ηnI(1+un,k)hn,n,kvn,k with u n , k , ∀ n , k u_{n,k}, \forall n,k un,k,∀n,k, v n , k , ∀ n , k v_{n,k}, \forall n,k vn,k,∀n,k, and η n , ∀ n {\eta _n}, \forall n ηn,∀n
直到 f q ( W , U , V ) {f_q}\left( {{\bf{W}},{\bf{U}},{\bf{V}}} \right) fq(W,U,V)收敛
对偶变量或Lagrange multipliers的更新:KKT条件 η n ( p ˉ n − ∑ k = 1 K w n , k H w n , k ) = 0 , ∀ n = 1 , … , N , {\eta _n}\left( {{{\bar p}_n} - \sum\limits_{k = 1}^K {{\bf{w}}_{n,k}^H{{\bf{w}}_{n,k}}} } \right) = 0,\forall n = 1, \ldots ,N, ηn(pˉn−k=1∑Kwn,kHwn,k)=0,∀n=1,…,N,
∑ k = 1 K w n , k H ( η n ) w n , k ( η n ) \sum\limits_{k = 1}^K {{\bf{w}}_{n,k}^H\left( {{\eta _n}} \right){{\bf{w}}_{n,k}}\left( {{\eta _n}} \right)} k=1∑Kwn,kH(ηn)wn,k(ηn)是关于 η n {\eta _n} ηn的单调递减函数,利用二分法可以找到最小的 η n {\eta _n} ηn ,使得 ∑ k = 1 K w n , k H ( η n ) w n , k ( η n ) = p ˉ n \sum\limits_{k = 1}^K {{\bf{w}}_{n,k}^H\left( {{\eta _n}} \right){{\bf{w}}_{n,k}}\left( {{\eta _n}} \right)} = {\bar p_n} k=1∑Kwn,kH(ηn)wn,k(ηn)=pˉn :
当 η n < η n ∗ {\eta _n} < \eta _n^* ηn<ηn∗时, ∑ k = 1 K w n , k H ( η n ) w n , k ( η n ) \sum\limits_{k = 1}^K {{\bf{w}}_{n,k}^H\left( {{\eta _n}} \right){{\bf{w}}_{n,k}}\left( {{\eta _n}} \right)} k=1∑Kwn,kH(ηn)wn,k(ηn) >基站最大发射功率 p ˉ n {\bar p_n} pˉn 。
当 η n = η n ∗ {\eta _n} = \eta _n^* ηn=ηn∗ 时, ∑ k = 1 K w n , k H ( η n ) w n , k ( η n ) \sum\limits_{k = 1}^K {{\bf{w}}_{n,k}^H\left( {{\eta _n}} \right){{\bf{w}}_{n,k}}\left( {{\eta _n}} \right)} k=1∑Kwn,kH(ηn)wn,k(ηn) =基站最大发射功率 p ˉ n {\bar p_n} pˉn 。
当 η n > η n ∗ {\eta _n} > \eta _n^* ηn>ηn∗时, ∑ k = 1 K w n , k H ( η n ) w n , k ( η n ) \sum\limits_{k = 1}^K {{\bf{w}}_{n,k}^H\left( {{\eta _n}} \right){{\bf{w}}_{n,k}}\left( {{\eta _n}} \right)} k=1∑Kwn,kH(ηn)wn,k(ηn) <基站最大发射功率 p ˉ n {\bar p_n} pˉn 。
Weighted MMSE
u n , k = h n , n , k H w n , k ∑ ( m , j ) ∣ h m , n , k H w m , j ∣ 2 + σ n , k 2 {u_{n,k}} = \frac{{{\bf{h}}_{n,n,k}^H{{\bf{w}}_{n,k}}}}{{\sum\limits_{(m,j)} {{{\left| {{\bf{h}}_{m,n,k}^H{{\bf{w}}_{m,j}}} \right|}^2}} + \sigma _{n,k}^2}} un,k=(m,j)∑∣hm,n,kHwm,j∣2+σn,k2hn,n,kHwn,k
(图中h的下标打错了)
hm,n,k denote the downlink channel between BS m and UE k in cell n
wn,k denote the beamformer for UE k in cell n
the optimum Lagrange multiplier μ n ⋆ \mu _n^ \star μn⋆ can be determined efficiently by a bisection search method.
原论文
Q. Shi, M. Razaviyayn, Z. -Q. Luo and C. He, “An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel,” in IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4331-4340, Sept. 2011, doi: 10.1109/TSP.2011.2147784.
V i k {{\bf{V}}_{{i_k}}} Vik 表示基站k对用户 i k {i_k} ik 的波束成形
H i k , j {{\bf{H}}_{{i_k},j}} Hik,j 表示从基站j到用户 i k {i_k} ik的信道
u k , i = h k , k , i H w k , i ∑ ( j , l ) ∣ h j , k , i H w j , l ∣ 2 + σ k , i 2 {u_{k,i}} = \frac{{{\bf{h}}_{k,k,i}^H{{\bf{w}}_{k,i}}}}{{\sum\limits_{(j,l)} {{{\left| {{\bf{h}}_{j,k,i}^H{{\bf{w}}_{j,l}}} \right|}^2}} + \sigma _{k,i}^2}} uk,i=(j,l)∑∣hj,k,iHwj,l∣2+σk,i2hk,k,iHwk,i
Lagrange dual
上海交通大学 CS257 Linear and Convex Optimization
南京大学 Duality (I) - NJU
the standard form (5.1)
min X f ( X ) s . t . g i ( X ) ≤ 0 , ∀ i = 1 , … , m , \begin{array}{l} {\mathop {\min }_{\bf{X}} \;\;f\left( {\bf{X}} \right)}\\ {{\rm{s}}.{\rm{t}}.\;{g_i}\left( {\bf{X}} \right) \le 0,\forall i = 1, \ldots ,m,} \end{array} minXf(X)s.t.gi(X)≤0,∀i=1,…,m,
5.1.1 The Lagrangian
the dual variables or Lagrange multiplier vectors associated with the problem (5.1).
5.1.2 The Lagrange dual function
the minimum value of the Lagrangian
5.2 The Lagrange dual problem
the Lagrange dual problem associated with the problem (5.1).
5.2.3 Strong duality and Slater’s constraint qualification
5.2.3 Strong duality and Slater’s constraint qualification
Slater’s theorem states that
strong duality holds, if Slater’s condition holds (and the problem is convex).
strong duality obtains, when the primal problem is convex and Slater’s condition holds
Slater’s Condition for Convex Problems
上海交通大学 CS257 Linear and Convex Optimization
5.5.3 KKT optimality conditions
Karush-Kuhn-Tucker (KKT) conditions
for any optimization problem with differentiable objective and constraint functions for which strong duality obtains, any pair of primal and dual optimal points must satisfy the KKT conditions (5.49).