A Surrogate Objective Framework for Prediction+Programming with Soft Constraints

1 Introduction . Mathematical optimization ( a.k.a . mathematical programming ) , e.g. , linear and quadratic programming , has been widely applied in decision-making processes , such as resource scheduling [ 1 ] , goods production planning [ 2 ] , portfolio optimization [ 3 ] , and power scheduling [ 4 ] . In practice , problem parameters ( e.g. , goods demands , and equity returns ) are often contextual and predicted by models with observed features ( e.g. , history time series ) . With the popularity of machine learning techniques and increasing available data , prediction+optimization has become a normal paradigm [ 5 ] . ∗Contributed during internship at Microsoft Research . † Corresponding Author . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . Prediction becomes critical to the performance of the full prediction+optimization workflow since modern optimization solvers ( e.g. , Gurobi [ 6 ] and CPLEX [ 7 ] ) can already efficiently find optimal solutions for most large scale optimization problems . Traditionally , prediction is treated separately as a general supervised learning problem and learned through minimizing a generic loss function ( e.g. , mean squared error for regression ) . However , studies have shown that minimization of the fitting errors does not necessarily lead to better final decision performance [ 8 , 9 , 10 , 11 ] . Recently , a lot of efforts on using optimization objective to guide the learning of prediction models have been made , which are decision-focused when training prediction models instead of using traditional prediction metrics , e.g . mean squared error losses . For linear objectives , the ‘ Smart Predict then Optimize ’ ( [ 10 ] ) proposes the SPO+ loss function to measure the prediction errors against optimization objectives , while direct optimization ( [ 12 ] ) updates the prediction model ’ s parameters by perturbation . For quadratic objectives , OptNet [ 13 , 14 ] implements the optimization as an implicit layer whose gradients can be computed by differentiating the KKT conditions and then back propagates to prediction neural network . CVXPY [ 15 ] uses similar technologies with OptNet but extends to more general cases of convex optimization . However , all the above state-of-the-art approaches do not contain the soft constraints in their objectives . In this paper , We consider linear ‘ soft-constraints ’ , a penalty in the form of max ( z , 0 ) , where z = Cx− d is a projection of decision variable x ∈ Rn and context variables C ∈ Rm×n , d ∈ Rm . Such soft constraints are often required in practice : for example , they could be the waste of provisioned resources over demands or the extra tax paid when violating regulatory rules . Unfortunately , the max ( · , 0 ) operator is not differentiable and thus can not be directly handled by these existing approaches . To differentiate soft constraints is a primary motivation of this paper . In this paper , we derive a surrogate objective framework for a broad set of real-world linear and quadratic programming problems with linear soft constraints and implement decision-focused differentiable predictions , with the assumption of non-negativity of hard constraint parameters . The framework consists of three steps : 1 ) rewriting all hard constraints into piece-wise linear soft constraints with a bounded penalty multiplier ; 2 ) using a differentiable element-wise surrogate to substitute the piece-wise objective , and solving the original function numerically to decide which segment the optimal point is on ; 3 ) analytically solving the local surrogate and obtaining the gradient ; the gradient is identical to that of the piecewise surrogate since the surrogate is convex/concave such that the optimal point is unique . Our main contributions are summarized as follows . First , we propose a differentiable surrogate objective function that incorporates both soft and hard constraints . As the foundation of our methodology , in Section 3 , we prove that , with reasonable assumptions generally satisfied in the real world , for linear and semi-definite negative quadratic programming problems , the constraints can be transformed into soft constraints ; then we propose an analytically differentiable surrogate function for the soft constraints max ( · , 0 ) . Second , we present the derived analytical and closed-form solutions for three representative optimization problems extended with soft constraints in Section 4 – linear programming with soft constraints , quadratic programming with soft constraints , and asymmetric soft constraint minimization . Unlike KKT-based differentiation methods , our method makes the calculation of gradients straightforward for predicting context parameters in any part of the problem . Finally , we apply with theoretical derivations and evaluate our approach in three scenarios , including synthetic linear programming , portfolio optimization , and resource provisioning in Section 4 , empirically demonstrate that our method outperforms two-stage and other predict+optimization approaches . 2 Preliminaries . 2.1 Real-world Optimization Problems with Soft Constraints . Our target is to solve the broad set of real-world mathematical optimization problems extended with a max ( z , 0 ) term in their objectives where z depends on decision variables and predicted context parameters . In practice , max ( z , 0 ) is very common ; for example , it may model overhead of underprovisioning , over-provisioning of goods , and penalty of soft regulation violations in investment portfolios . We call the above max ( z , 0 ) term in an objective as soft constraints , where z ≤ 0 is allowed to violate as long as the objective improves . The general problem formulation is max x g ( θ , x ) − αT max ( z , 0 ) , z = Cx− d , s.t . Ax ≤ b , Bx = c , x ≥ 0 ( 1 ) where g is a utility function , x ∈ Rn the decision variable , and θ ∈ Rn the predicted parameters . Based on observations on a broad set of practical problem settings , we impose two assumptions on the formulation , which serves as the basis of following derivations in this paper . First , we assume A ∈ Rm1×n ≥ 0 , b ∈ Rm1 ≥ 0 , B ∈ Rm2×n ≥ 0 , and c ∈ Rm2 ≥ 0 hold . This is because for problems with constraint on weights , quantities or their thresholds , these parameters are naturally non-negative . Second , we assume the linearity of soft constraints , that is z = Cx − d , where z ∈ Rm3 , C ∈ Rm3×n , and d ∈ Rm3 . This form of soft constraints make sense in wide application situations when describing the penalty of goods under-provisioning or over-provisioning , a vanish in marginal profits , or running out of current materials . Now we look into three representative instances of Eq.1 , extracted from real-world applications . Linear programming with soft constraints , where g ( x , θ ) = θTx . The problem formulation is max x θTx− αT max ( Cx− d , 0 ) , s.t . Ax ≤ b , Bx = c , x ≥ 0 ( 2 ) where α ≥ 0 . Consider the application of logistics where θ represents goods ’ prices , { A , B , C } are the capability of transportation tools , and { b , c } are the thresholds . Obviously , A , b , B , c ≥ 0 hold . Quadratic programming with soft constraints , where g ( θ , x ) = θTx − xTQx . One example is the classic minimum variance portfolio problem [ 16 ] with semi-definite positive covariance matrix Q and expected return θ to be predicted . We extend it with soft constraints which , for example , may represent regulations on portions of equities for some fund types . Formally with α ≥ 0 , we have : max x θTx− xTQx− αT max ( Cx− d , 0 ) , s.t . Bx = c , x ≥ 0 . ( 3 ) Optimization of asymmetric soft constraints . This set of optimization problems have the objective to match some expected quantities by penalizing excess and deficiency with probably different weights . Such formulation represents widespread resource provisioning problems , e.g. , power [ 4 ] and cloud resources [ 1 ] , where we minimize the cost of under-provisioning and over-provisioning against demands . Formally with α1 , α2 > 0 , we have : max x − ( αT1 max ( Cx− d , 0 ) + αT2 max ( d− Cx , 0 ) ) , s.t . Bx = c , x ≥ 0 . ( 4 ) In this paper , we consider a challenging task where C is a matrix to be predicted with known constants d. In reality , the Cx− d term may represent the `` wasted '' part when satisfying the actual need of d . 2.2 Prediction+Optimization . For compactness we write Eq.1 as maxx∈X f ( x , θ ) , where f is the objective function and X is the feasible domain . The solver for f is to solve x∗ = argmaxx∈X f ( x , θ ) . With parameters θ known , Eq.2–4 can be solved by mature solvers like Gurobi [ 6 ] and CPLEX [ 7 ] . In prediction+optimization , θ is unknown and needs to be predicted from some observed features ξ ∈ Ξ . The prediction model is trained on a dataset D = { ( ξi , θi ) } Ni=1 . In this paper , we consider the prediction model , namely Φ , as a neural network parameterized with ψ . In traditional supervised learning , Φψ is learned by minimizing a generic loss , e.g. , L1 ( Mean Absolute Error ) or L2 ( Mean Squared Error ) , which measures the expected distance between predictions and real values . However , such loss minimization is often inconsistent with the optimization objective f , especially when the prediction model is biased [ 11 ] . Instead , decision-focused learning directly optimizes ψ with respect to the optimization objective f , that is , maxψ E ( ξ , θ ) ∼D [ f ( x̂∗ ( Φψ ( ξ ) ) , θ ) ] . The full computational flow is illustrated in Fig.1 . In the gradient-based learning , update of ψ ’ s gradient is ∂r∂ψ = ∂θ̂ ∂ψ ∂x̂∗ ∂θ̂ ∂r ∂x̂∗ , where utility r = f ( x̂∗ , θ ) . The Jacobian ∂θ̂/∂ψ is computed implicitly by auto-differentiation of deep learning frameworks ( e.g , PyTorch [ 17 ] ) , and ∂r/∂x̂∗ is analytical . The main challenge is to compute ∂x̂∗/∂θ̂ , which depends on differentiating the argmax operation . One recent approach is to rewrite the objective to be convex ( by adding a quadratic regularizer if necessary ) , build and differentiate the optimality conditions ( e.g. , KKT conditions ) [ 18 ] which map θ̂ to the solution x̂∗ , and then apply implicit function theorem to obtain ∂x̂∗/∂θ̂ . Alternatively , in this paper we propose a novel approach that rewrites the problem as an unconstrained problem with soft constraints and derives analytical solutions , based on our observations on the real-world problem structures and coefficient properties . 3 Methodology . Our main idea is to derive a surrogate function for f with a closed-form solution such that the Jacobian ∂x∂θ is analytical , making the computation of gradient straightforward . Unlike other recent work [ 13 , 14 , 15 , 3 ] , our method does not need to solve KKT optimality condition system . Instead , by adding reasonable costs for infeasibility , we convert the constrained problem into an unconstrained one . With the assumption of concavity , we prove that there exist constant vectors β1 , β2 , β3 , such that Eq.1 can be equivalently transformed into an unconstrained problem : max x L ( x ) = max x g ( x , θ ) −αT max ( Cx−d , 0 ) −βT1 max ( Ax−b , 0 ) −βT2 |Bx−c|−βT3 max ( −x , 0 ) ( 5 ) The structure of this section is as follows . Section 3.1 proves that the three types of hard constraints can be softened by deriving bounds of β1 , β2 , β3 ; for this paper , we will assign each entry of the three vectors the equal value ( with a slight abuse of notation , we denote β1 = β2 = β3 = β for the proofs of bounds ; we align with the worst bound applicable to the problem formulation . ) Section 3.2 proposes a novel surrogate function of max ( · , 0 ) , such that the analytical form of ∂x∂θ can be easily derived via techniques of implicit differentiation [ 18 ] and matrix differential calculus [ 19 ] on equations derived by convexity [ 20 ] . Based on such derived ∂x∂θ , we develop our end-to-end learning algorithm of prediction+optimization whose detailed procedure is described in Appendix C .

Summary: This paper presents an approach for "predict, then optimize" problems that is amenable to derivative-based learning methods. It involves converting hard constraints into soft constraints (i.e. implicitly enforced through objective penalization). The main theoretical contributions are bounds on the magnitude of objective change that can be attributed to infeasibility with respect to the original hard constraints.