In movement neuroscience the motor synergy hypothesis has been proposed as the simplifying strategy that the brain adopts when facing redundant tasks. By grouping multiple control variables into synergies, the brain reduces the number of degrees-of-freedom effectively available to solve a certain task. Kinematic, or postural synergies have been identified during the execution of pointing tasks involving either the eye, the head or the wrist and during hand grasping. Postural synergies can be predicted via constrained optimization by hypothesizing the existence of cost functions that the brain would minimize during the execution of redundant tasks. From a computational perspective, in the hypothesis of a correct guess for the cost function, the challenge remains of how to tune the cost parameters so as to predict experimental synergies. In this work a postural model for the wrist-forearm previously proposed in the literature is extended with a non-linear inverse optimization (NIO) approach to tune the discomfort function parameters of the model. An efficient method is proposed to filter and down-sample the experimental data so as to reduce the computational burden of the NIO algorithm. Results show that, after the optimization of the cost parameters, the model can predict with high accuracy six experimental pointing strategies. The proposed approach may in future find applications in human-like motion planning for redundant robots.